An experimental method to deal with continuous predictors and possible alternative functional relationships between them and the trait of interest. In this case, we are modeling vessel size in response to environmental temperature.
## Load in libraries
library(bayou)
Loading required package: ape
Loading required package: geiger
Attaching package: ‘geiger’
The following object is masked _by_ ‘.GlobalEnv’:
mkn.lik
Loading required package: phytools
Loading required package: maps
Loading required package: coda
Attaching package: ‘bayou’
The following object is masked from ‘package:grDevices’:
colorRamp
library(treeplyr)
Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Attaching package: ‘treeplyr’
The following object is masked from ‘package:stats’:
reorder
library(diversitree)
Attaching package: ‘diversitree’
The following object is masked from ‘package:dplyr’:
combine
The following object is masked from ‘package:coda’:
mcmc
library(optimx)
## Set working directory
setwd("~/repos/growthforms/analysis")
td <- readRDS("../output/cleandata/tdOUwie.rds")
td
$phy
Phylogenetic tree with 351 tips and 350 internal nodes.
Tip labels:
Tsuga_canadensis, Abies_concolor, Pseudotsuga_menziesii, Picea_smithiana, Pinus_longaeva, Pinus_cembra, ...
Node labels:
Spermatophyta, , Pinales, , , , ...
Rooted; includes branch lengths.
$dat
attr(,"class")
[1] "treedata" "list"
attr(,"tip.label")
[1] "Tsuga_canadensis" "Abies_concolor" "Pseudotsuga_menziesii" "Picea_smithiana"
[5] "Pinus_longaeva" "Pinus_cembra" "Pinus_wallichiana" "Pinus_halepensis"
[9] "Pinus_roxburghii" "Pinus_mugo" "Pinus_sylvestris" "Pinus_contorta"
[13] "Pinus_maximinoi" "Pinus_radiata" "Pinus_patula" "Thuja_occidentalis"
[17] "Juniperus_oxycedrus" "Juniperus_communis" "Prumnopitys_ferruginea" "Prumnopitys_taxifolia"
[21] "Dacrycarpus_dacrydioides" "Podocarpus_totara" "Ascarina_lucida" "Pseudowintera_colorata"
[25] "Hedycarya_arborea" "Eusideroxylon_zwageri" "Beilschmiedia_tawa" "Laurus_azorica"
[29] "Laurus_nobilis" "Litsea_calicaris" "Nectandra_megapotamica" "Ocotea_puberula"
[33] "Virola_surinamensis" "Pycnanthus_angolensis" "Magnolia_grandiflora" "Magnolia_tripetala"
[37] "Magnolia_acuminata" "Buxus_sempervirens" "Platanus_orientalis" "Knightia_excelsa"
[41] "Liquidambar_styraciflua" "Stachyurus_himalaicus" "Terminalia_amazonia" "Terminalia_ivorensis"
[45] "Punica_granatum" "Duabanga_moluccana" "Sonneratia_alba" "Neomyrtus_pedunculata"
[49] "Lophomyrtus_obcordata" "Leptospermum_scoparium" "Kunzea_ericoides" "Eucalyptus_globoidea"
[53] "Eucalyptus_regnans" "Eucalyptus_obliqua" "Eucalyptus_sieberi" "Eucalyptus_grandis"
[57] "Eucalyptus_globulus" "Salvadora_persica" "Capparis_spinosa" "Thymelaea_hirsuta"
[61] "Shorea_leprosula" "Shorea_ovata" "Guazuma_ulmifolia" "Apeiba_tibourbou"
[65] "Apeiba_membranacea" "Grewia_villosa" "Sterculia_apetala" "Cavanillesia_platanifolia"
[69] "Ceiba_pentandra" "Spondias_mombin" "Tapirira_guianensis" "Anacardium_excelsum"
[73] "Mangifera_indica" "Rhus_glabra" "Rhus_coriaria" "Pistacia_lentiscus"
[77] "Pistacia_atlantica" "Billia_hippocastanum" "Acer_cappadocicum" "Harpullia_cupanioides"
[81] "Sapindus_saponaria" "Allophylus_cobbe" "Nephelium_lappaceum" "Litchi_chinensis"
[85] "Alectryon_excelsus" "Swietenia_macrophylla" "Carapa_procera" "Carapa_grandiflora"
[89] "Carapa_guianensis" "Cedrela_odorata" "Cabralea_canjerana" "Simarouba_amara"
[93] "Bulnesia_arborea" "Eucryphia_cordifolia" "Eucryphia_lucida" "Weinmannia_trichosperma"
[97] "Aristotelia_serrata" "Elaeocarpus_hookerianus" "Elaeocarpus_japonicus" "Goupia_glabra"
[101] "Caryocar_glabrum" "Bischofia_javanica" "Hieronyma_alchorneoides" "Erythroxylum_confusum"
[105] "Erythroxylum_macrophyllum" "Erythroxylum_panamense" "Mammea_africana" "Calophyllum_brasiliense"
[109] "Symphonia_globulifera" "Drypetes_lateriflora" "Salix_alba" "Salix_triandra"
[113] "Salix_alaxensis" "Pera_glabrata" "Hevea_brasiliensis" "Gymnanthes_lucida"
[117] "Hura_crepitans" "Euphorbia_tirucalli" "Nothofagus_menziesii" "Nothofagus_fusca"
[121] "Nothofagus_solandri" "Nothofagus_dombeyi" "Fagus_sylvatica" "Quercus_semecarpifolia"
[125] "Quercus_coccifera" "Quercus_faginea" "Castanopsis_indica" "Quercus_ilex"
[129] "Myrica_esculenta" "Juglans_regia" "Juglans_australis" "Casuarina_equisetifolia"
[133] "Casuarina_cunninghamiana" "Carpinus_betulus" "Betula_utilis" "Betula_nana"
[137] "Betula_pubescens" "Betula_pendula" "Krugiodendron_ferreum" "Rhamnus_alaternus"
[141] "Rhamnus_cathartica" "Ziziphus_mistol" "Ziziphus_mucronata" "Ziziphus_jujuba"
[145] "Ziziphus_lotus" "Aphananthe_aspera" "Gironniera_subaequalis" "Trema_cannabina"
[149] "Trema_tomentosa" "Trema_orientalis" "Milicia_excelsa" "Morus_alba"
[153] "Morus_rubra" "Maclura_pomifera" "Maclura_cochinchinensis" "Maclura_tricuspidata"
[157] "Broussonetia_papyrifera" "Brosimum_lactescens" "Brosimum_alicastrum" "Brosimum_guianense"
[161] "Brosimum_utile" "Brosimum_rubescens" "Antiaris_toxicaria" "Maquira_calophylla"
[165] "Pseudolmedia_laevigata" "Perebea_guianensis" "Ficus_carica" "Rubus_swinhoei"
[169] "Rubus_idaeus" "Hagenia_abyssinica" "Sarcopoterium_spinosum" "Polylepis_australis"
[173] "Rosa_canina" "Adenostoma_sparsifolium" "Adenostoma_fasciculatum" "Sorbaria_sorbifolia"
[177] "Holodiscus_discolor" "Spiraea_salicifolia" "Lyonothamnus_floribundus" "Oemleria_cerasiformis"
[181] "Prunus_lusitanica" "Prunus_laurocerasus" "Prunus_serotina" "Prunus_virginiana"
[185] "Prunus_mahaleb" "Prunus_spinulosa" "Prunus_avium" "Prunus_davidiana"
[189] "Prunus_persica" "Prunus_spinosa" "Prunus_cerasifera" "Vauquelinia_californica"
[193] "Sorbus_americana" "Sorbus_aucuparia" "Mespilus_germanica" "Amelanchier_alnifolia"
[197] "Photinia_villosa" "Sorbus_aria" "Malus_pumila" "Malus_baccata"
[201] "Photinia_davidiana" "Pyrus_calleryana" "Rhaphiolepis_indica" "Eriobotrya_japonica"
[205] "Apuleia_leiocarpa" "Castanospermum_australe" "Tetraberlinia_bifoliolata" "Tamarindus_indica"
[209] "Hymenaea_courbaril" "Baikiaea_plurijuga" "Caesalpinia_paraguariensis" "Erythrophleum_ivorense"
[213] "Prosopis_africana" "Acacia_nilotica" "Acacia_tortilis" "Acacia_gerrardii"
[217] "Acacia_karroo" "Acacia_polyacantha" "Acacia_nigrescens" "Acacia_erioloba"
[221] "Cedrelinga_cateniformis" "Albizia_procera" "Pterocarpus_officinalis" "Anagyris_foetida"
[225] "Sophora_microphylla" "Spartium_junceum" "Lecointea_amazonica" "Bocoa_prouacensis"
[229] "Swartzia_polyphylla" "Swartzia_simplex" "Swartzia_arborescens" "Swartzia_panacoco"
[233] "Swartzia_cardiosperma" "Minquartia_guianensis" "Ochanostachys_amentacea" "Alepis_flavida"
[237] "Tamarix_aphylla" "Salsola_vermiculata" "Alangium_chinense" "Alangium_villosum"
[241] "Nyssa_sylvatica" "Nyssa_aquatica" "Norantea_guianensis" "Ficalhoa_laurifolia"
[245] "Eurya_japonica" "Gordonia_lasianthus" "Schima_wallichii" "Camellia_japonica"
[249] "Camellia_sinensis" "Camellia_caudata" "Camellia_brevistyla" "Camellia_oleifera"
[253] "Sideroxylon_obtusifolium" "Manilkara_bidentata" "Symplocos_paniculata" "Symplocos_anomala"
[257] "Symplocos_lucida" "Symplocos_sumuntia" "Symplocos_cochinchinensis" "Symplocos_glomerata"
[261] "Symplocos_tinctoria" "Symplocos_martinicensis" "Bertholletia_excelsa" "Pterostyrax_hispidus"
[265] "Halesia_carolina" "Styrax_camporum" "Styrax_officinalis" "Arbutus_andrachne"
[269] "Rhododendron_anthopogon" "Dracophyllum_strictum" "Sprengelia_incarnata" "Trochocarpa_laurina"
[273] "Monotoca_elliptica" "Oxydendrum_arboreum" "Lyonia_lucida" "Lyonia_ovalifolia"
[277] "Pieris_formosa" "Pieris_japonica" "Gaultheria_shallon" "Gaultheria_mucronata"
[281] "Zenobia_pulverulenta" "Andromeda_polifolia" "Ilex_crenata" "Ilex_dumosa"
[285] "Ilex_decidua" "Ilex_macfadyenii" "Ilex_opaca" "Ilex_aquifolium"
[289] "Ilex_integra" "Ilex_latifolia" "Ilex_canariensis" "Ilex_coriacea"
[293] "Ilex_glabra" "Ilex_chinensis" "Ilex_theezans" "Ilex_guianensis"
[297] "Ilex_anomala" "Ilex_micrococca" "Ilex_rotunda" "Ilex_verticillata"
[301] "Ilex_mitis" "Ilex_goshiensis" "Sambucus_nigra" "Sambucus_racemosa"
[305] "Viburnum_dilatatum" "Viburnum_opulus" "Viburnum_tinus" "Viburnum_odoratissimum"
[309] "Lonicera_maackii" "Lonicera_chrysantha" "Pennantia_corymbosa" "Aralidium_pinnatifidum"
[313] "Griselinia_littoralis" "Schefflera_morototoni" "Hedera_helix" "Schefflera_volkensii"
[317] "Schefflera_digitata" "Carpodetus_serratus" "Garrya_elliptica" "Aucuba_japonica"
[321] "Fagraea_fragrans" "Aspidosperma_quebracho-blanco" "Alstonia_boonei" "Alstonia_scholaris"
[325] "Nerium_oleander" "Uncaria_tomentosa" "Vangueria_infausta" "Genipa_americana"
[329] "Nicotiana_glauca" "Haenianthus_salicifolius" "Olea_capensis" "Olea_europaea"
[333] "Phillyrea_latifolia" "Syringa_reticulata" "Ligustrum_japonicum" "Ligustrum_lucidum"
[337] "Ligustrum_sinense" "Jasminum_fruticans" "Avicennia_marina" "Trichanthera_gigantea"
[341] "Paulownia_tomentosa" "Jacaranda_copaia" "Tecoma_stans" "Spathodea_campanulata"
[345] "Newbouldia_laevis" "Catalpa_bignonioides" "Catalpa_speciosa" "Mansoa_verrucifera"
[349] "Tabebuia_rosea" "Tectona_grandis" "Vitex_agnus-castus"
attr(,"dropped")
attr(,"dropped")$dropped_from_tree
[1] "Myrtus_communis" "Nyctanthes_arbor-tristis" "Paliurus_spina-christi" "Ruyschia_tremadena"
attr(,"dropped")$dropped_from_data
[1] "Blasia_pusilla" "Lunularia_cruciata" "Marchantia_polymorpha" "Riccia_fluitans"
[5] "Reboulia_hemisphaerica" "Marchantia_foliacea" "Conocephalum_conicum" "Haplomitrium_gibbsiae"
[9] "Fossombronia_pusilla" "Pellia_endiviifolia" "Pellia_epiphylla" "Riccardia_multifida"
[13] "Riccardia_latifrons" "Aneura_pinguis" "Aneura_mirabilis" "Metzgeria_conjugata"
[17] "Metzgeria_furcata" "Metzgeria_pubescens" "Metzgeria_temperata" "Porella_pinnata"
[21] "Porella_cordaeana" "Porella_platyphylla" "Porella_obtusata" "Porella_arboris"
[25] "Frullania_dilatata" "Frullania_microphylla" "Frullania_fragilifolia" "Frullania_teneriffae"
[29] "Frullania_tamarisci" "Radula_aquilegia" "Radula_complanata" "Jubula_hutchinsiae"
[33] "Lejeunea_lamacerina" "Lejeunea_cavifolia" "Blepharostoma_trichophyllum" "Herbertus_borealis"
[37] "Lepidozia_reptans" "Kurzia_trichoclados" "Bazzania_tricrenata" "Bazzania_trilobata"
[41] "Lophocolea_heterophylla" "Lophocolea_bidentata" "Chiloscyphus_pallescens" "Pedinophyllum_interruptum"
[45] "Plagiochila_spinulosa" "Plagiochila_punctata" "Plagiochila_asplenioides" "Plagiochila_porelloides"
[49] "Cephaloziella_divaricata" "Odontoschisma_denudatum" "Nowellia_curvifolia" "Lophozia_ventricosa"
[53] "Diplophyllum_albicans" "Diplophyllum_obtusifolium" "Anastrophyllum_minutum" "Jungermannia_exsertifolia"
[57] "Barbilophozia_barbata" "Barbilophozia_hatcheri" "Calypogeia_arguta" "Harpanthus_flotovianus"
[61] "Harpanthus_scutatus" "Jungermannia_obovata" "Nardia_scalaris" "Gymnomitrion_concinnatum"
[65] "Marsupella_emarginata" "Geocalyx_graveolens" "Jungermannia_pumila" "Jungermannia_atrovirens"
[69] "Saccogyna_viticulosa" "Calypogeia_fissa" "Calypogeia_integristipula" "Calypogeia_neesiana"
[73] "Polytrichum_commune" "Sphagnum_contortum" "Sphagnum_rubellum" "Sphagnum_squarrosum"
[77] "Sphagnum_fuscum" "Sphagnum_palustre" "Sphagnum_recurvum" "Sphagnum_fallax"
[81] "Sphagnum_cuspidatum" "Entosthodon_laevis" "Physcomitrella_patens" "Calymperes_palisotii"
[85] "Goniomitrium_acuminatum" "Dicranum_scoparium" "Philonotis_fontana" "Plagiothecium_curvifolium"
[89] "Sanionia_uncinata" "Brachythecium_salebrosum" "Brachythecium_acuminatum" "Brachythecium_rutabulum"
[93] "Hylocomium_splendens" "Pleurozium_schreberi" "Hypnum_cupressiforme" "Palustriella_falcata"
[97] "Anomodon_viticulosus" "Jaegerina_scariosa" "Hamatocaulis_vernicosus" "Phaeoceros_laevis"
[101] "Anthoceros_formosae" "Anthoceros_punctatus" "Huperzia_phyllantha" "Huperzia_drummondii"
[105] "Huperzia_australiana" "Huperzia_selago" "Huperzia_lucidula" "Lycopodiella_inundata"
[109] "Lycopodiella_cernua" "Lycopodium_casuarinoides" "Lycopodium_clavatum" "Lycopodium_deuterodensum"
[113] "Lycopodium_volubile" "Austrolycopodium_fastigiatum" "Diphasium_scariosum" "Lycopodium_annotinum"
[117] "Lycopodium_obscurum" "Diphasiastrum_complanatum" "Diphasiastrum_alpinum" "Isoetes_schweinfurthii"
[121] "Cephaloceraton_histrix" "Isoetes_echinospora" "Isoetes_lacustris" "Selaginella_selaginoides"
[125] "Selaginella_kraussiana" "Selaginella_remotifolia" "Selaginella_pygmaea" "Selaginella_uliginosa"
[129] "Selaginella_gracillima" "Selaginella_delicatissima" "Selaginella_helvetica" "Selaginella_frondosa"
[133] "Selaginella_longipinna" "Selaginella_moellendorffii" "Selaginella_tamariscina" "Selaginella_biformis"
[137] "Marattia_douglasii" "Angiopteris_wangii" "Angiopteris_hokouensis" "Angiopteris_caudatiformis"
[141] "Angiopteris_evecta" "Tmesipteris_truncata" "Psilotum_nudum" "Tmesipteris_tannensis"
[145] "Tmesipteris_obliqua" "Tmesipteris_elongata" "Botrychium_virginianum" "Botrychium_lanuginosum"
[149] "Botrychium_multifidum" "Botrychium_schaffneri" "Botrychium_matricariifolium" "Botrychium_lunaria"
[153] "Ophioglossum_nudicaule" "Ophioglossum_costatum" "Ophioglossum_pendulum" "Ophioglossum_petiolatum"
[157] "Ophioglossum_lusitanicum" "Ophioglossum_vulgatum" "Ophioglossum_reticulatum" "Equisetum_diffusum"
[161] "Equisetum_fluviatile" "Equisetum_telmateia" "Equisetum_palustre" "Equisetum_sylvaticum"
[165] "Equisetum_pratense" "Equisetum_arvense" "Equisetum_scirpoides" "Equisetum_variegatum"
[169] "Equisetum_ramosissimum" "Equisetum_hyemale" "Equisetum_laevigatum" "Equisetum_giganteum"
[173] "Osmunda_claytoniana" "Osmunda_vachellii" "Osmunda_japonica" "Osmunda_regalis"
[177] "Todea_barbara" "Leptopteris_fraseri" "Leptopteris_hymenophylloides" "Leptopteris_superba"
[181] "Dicranopteris_linearis" "Dicranopteris_pedata" "Dicranopteris_gigantea" "Diplopterygium_glaucoides"
[185] "Gleichenia_japonica" "Diplopterygium_glaucum" "Sticherus_laevigatus" "Sticherus_cunninghamii"
[189] "Gleichenia_dicarpa" "Gleichenia_microphylla" "Trichomanes_diversifrons" "Trichomanes_pinnatum"
[193] "Cephalomanes_javanicum" "Abrodictyum_rigidum" "Trichomanes_borbonicum" "Polyphlebium_venosum"
[197] "Didymoglossum_reptans" "Didymoglossum_erosum" "Crepidomanes_latealatum" "Crepidomanes_auriculatum"
[201] "Vandenboschia_collariata" "Hymenophyllum_pulcherrimum" "Hymenophyllum_poolii" "Hymenophyllum_flabellatum"
[205] "Hymenophyllum_scabrum" "Hymenophyllum_villosum" "Hymenophyllum_sanguinolentum" "Hymenophyllum_dilatatum"
[209] "Hymenophyllum_demissum" "Hymenophyllum_flexuosum" "Hymenophyllum_australe" "Hymenophyllum_minimum"
[213] "Hymenophyllum_darwinii" "Hymenophyllum_polyanthos" "Hymenophyllum_sibthorpioides" "Hymenophyllum_tunbrigense"
[217] "Hymenophyllum_peltatum" "Hymenophyllum_revolutum" "Hymenophyllum_cupressiforme" "Hymenophyllum_pumilum"
[221] "Schizaea_fistulosa" "Anemia_phyllitidis" "Schizaea_pectinata" "Schizaea_dichotoma"
[225] "Lygodium_japonicum" "Lygodium_circinatum" "Lygodium_kerstenii" "Cibotium_barometz"
[229] "Cibotium_chamissoi" "Cibotium_glaucum" "Calochlaena_dubia" "Dicksonia_fibrosia"
[233] "Balantium_antarcticum" "Dicksonia_baudouinii" "Dicksonia_squarrosa" "Cyathea_leichhardtiana"
[237] "Sphaeropteris_cooperi" "Cyathea_contaminans" "Sphaeropteris_brunoniana" "Cyathea_alata"
[241] "Cyathea_karsteniana" "Cyathea_senilis" "Cyathea_spinulosa" "Cyathea_australis"
[245] "Alsophila_cuspidata" "Cyathea_colensoi" "Salvinia_natans" "Salvinia_adnata"
[249] "Azolla_nilotica" "Azolla_pinnata" "Azolla_filiculoides" "Azolla_mexicana"
[253] "Azolla_microphylla" "Azolla_caroliniana" "Calamistrum_globuliferum" "Pilularia_novae"
[257] "Marsilea_mutica" "Marsilea_minuta" "Marsilea_quadrifolia" "Marsilea_drummondii"
[261] "Marsilea_capensis" "Marsilea_coromandelina" "Marsilea_villifolia" "Marsilea_vera"
[265] "Marsilea_aegyptiaca" "Marsilea_ephippiocarpa" "Marsilea_farinosa" "Marsilea_macrocarpa"
[269] "Saccoloma_elegans" "Saccoloma_inaequale" "Lindsaea_odorata" "Lindsaea_javanensis"
[273] "Lindsaea_ensifolia" "Cryptogramma_crispa" "Pityrogramma_calomelanos" "Anogramma_leptophylla"
[277] "Pteris_platyzomopsis" "Pteris_vittata" "Onychium_japonicum" "Pteris_tremula"
[281] "Pteris_semipinnata" "Pteris_setulosocostulata" "Pteris_ensiformis" "Pteris_excelsa"
[285] "Pteris_cretica" "Pteris_multifida" "Pteris_berteroana" "Pteris_microptera"
[289] "Cheilanthes_lanosa" "Pellaea_falcata" "Pellaea_rotundifolia" "Pellaea_boivinii"
[293] "Pellaea_dura" "Cheilanthes_argentea" "Pellaea_calomelanos" "Cheilanthes_multifida"
[297] "Cheilanthes_hirta" "Cheilanthes_eckloniana" "Cheilanthes_sieberi" "Cheilanthes_lasiophylla"
[301] "Cheilanthes_distans" "Anetium_citrifolium" "Vittaria_isoetifolia" "Vittaria_lineata"
[305] "Vittaria_stipitata" "Hecistopteris_pumila" "Haplopteris_volkensii" "Vittaria_ensiformis"
[309] "Vittaria_flexuosa" "Adiantum_capillus-veneris" "Adiantum_raddianum" "Adiantum_chilense"
[313] "Adiantum_hispidulum" "Adiantum_diaphanum" "Adiantum_pedatum" "Adiantum_obliquum"
[317] "Adiantum_latifolium" "Adiantum_flabellulatum" "Adiantum_bonatianum" "Adiantum_lunulatum"
[321] "Adiantum_caudatum" "Adiantum_malesianum" "Monachosorum_henryi" "Pteridium_aquilinum"
[325] "Pteridium_esculentum" "Histiopteris_incisa" "Hypolepis_millefolium" "Hypolepis_punctata"
[329] "Hypolepis_muelleri" "Dennstaedtia_punctilobula" "Dennstaedtia_scabra" "Microlepia_strigosa"
[333] "Microlepia_platyphylla" "Microlepia_speluncae" "Microlepia_hookeriana" "Microlepia_marginata"
[337] "Hymenasplenium_cardiophyllum" "Hymenasplenium_hondoense" "Asplenium_cheilosorum" "Hymenasplenium_excisum"
[341] "Asplenium_filipes" "Asplenium_unilaterale" "Asplenium_juglandifolium" "Ceterach_aurea"
[345] "Asplenium_ensiforme" "Asplenium_cuneatiforme" "Asplenium_caudatum" "Asplenium_polyodon"
[349] "Asplenium_sphenotomum" "Asplenium_affine" "Asplenium_wilfordii" "Asplenium_pseudowilfordii"
[353] "Asplenium_finlaysonianum" "Asplenium_yoshinagae" "Asplenium_nidus" "Asplenium_setoi"
[357] "Cyclosorus_parasiticus" "Asplenium_phyllitidis" "Asplenium_antrophyoides" "Asplenium_cymbifolium"
[361] "Asplenium_antiquum" "Asplenium_anisophyllum" "Asplenium_chathamense" "Asplenium_lamprophyllum"
[365] "Asplenium_shuttleworthianum" "Asplenium_dimorphum" "Asplenium_difforme" "Asplenium_hookerianum"
[369] "Asplenium_richardii" "Asplenium_viellardii" "Asplenium_oblongifolium" "Asplenium_obliquum"
[373] "Asplenium_flaccidum" "Asplenium_obtusatum" "Asplenium_simplicifrons" "Asplenium_prolongatum"
[377] "Asplenium_griffithianum" "Asplenium_tenerum" "Asplenium_rutifolium" "Asplenium_theciferum"
[381] "Asplenium_loxoscaphoides" "Asplenium_feei" "Asplenium_elliottii" "Asplenium_smedsii"
[385] "Asplenium_mannii" "Asplenium_christii" "Asplenium_sandersonii" "Asplenium_dregeanum"
[389] "Asplenium_gemmiferum" "Asplenium_lolegnamense" "Asplenium_parvifolium" "Asplenium_octoploideum"
[393] "Phyllitopsis_hybrida" "Ceterach_officinarum" "Asplenium_cyprium" "Asplenium_hemionitis"
[397] "Asplenium_septentrionale" "Asplenium_seelosii" "Asplenium_davallioides" "Asplenium_bulbiferum"
[401] "Asplenium_wrightii" "Asplenium_wrightioides" "Asplenium_variabile" "Asplenium_protensum"
[405] "Asplenium_aethiopicum" "Asplenium_praemorsum" "Asplenium_praegracile" "Asplenium_volkensii"
[409] "Asplenium_hemitomum" "Asplenium_friesiorum" "Asplenium_punjabense" "Asplenium_dalhousiae"
[413] "Asplenium_sagittatum" "Asplenium_scolopendrium" "Asplenium_hispanicum" "Asplenium_ruta"
[417] "Asplenium_onopteris" "Asplenium_cuneifolium" "Asplenium_montanum" "Asplenium_adiantum"
[421] "Asplenium_fissum" "Asplenium_aegaeum" "Asplenium_sulcatum" "Asplenium_abscissum"
[425] "Asplenium_hostmannii" "Asplenium_peruvianum" "Asplenium_pteropus" "Asplenium_cristatum"
[429] "Asplenium_myriophyllum" "Asplenium_lunulatum" "Asplenium_erectum" "Asplenium_viride"
[433] "Asplenium_hobdyi" "Asplenium_normale" "Asplenium_trichomanes" "Asplenium_tripteropus"
[437] "Asplenium_formosum" "Asplenium_heterochroum" "Asplenium_resiliens" "Asplenium_monanthes"
[441] "Asplenium_hallbergii" "Asplenium_dareoides" "Asplenium_flabellifolium" "Asplenium_pauperequitum"
[445] "Dicksonia_sellowiana" "Asplenium_haughtonii" "Asplenium_phillipsianum" "Asplenium_cordatum"
[449] "Asplenium_interjectum" "Asplenium_lushanense" "Asplenium_laciniatum" "Asplenium_varians"
[453] "Asplenium_tenuicaule" "Asplenium_pekinense" "Asplenium_sarelii" "Asplenium_platyneuron"
[457] "Asplenium_bourgaei" "Asplenium_jahandiezii" "Asplenium_ruprechtii" "Asplenium_radicans"
[461] "Asplenium_yunnanense" "Asplenium_foresiense" "Asplenium_incisum" "Asplenium_petrarchae"
[465] "Asplenium_majoricum" "Gymnocarpium_dryopteris" "Acystopteris_tenuisecta" "Cystopteris_fragilis"
[469] "Cystopteris_sudetica" "Macrothelypteris_torresiana" "Phegopteris_hexagonoptera" "Thelypteris_confluens"
[473] "Oreopteris_limbosperma" "Pronephrium_simplex" "Parathelypteris_beddomei" "Thelypteris_globulifera"
[477] "Thelypteris_noveboracensis" "Diplazium_wichurae" "Anisocampium_niponicum" "Athyrium_schimperi"
[481] "Athyrium_yokoscense" "Athyrium_alpestre" "Athyrium_filix" "Onoclea_sensibilis"
[485] "Matteuccia_struthiopteris" "Blechnum_orientale" "Sadleria_cyatheoides" "Blechnum_spicant"
[489] "Blechnum_occidentale" "Blechnum_patersonii" "Blechnum_capense" "Doodia_caudata"
[493] "Doodia_aspera" "Blechnum_medium" "Lomariopsis_spectabilis" "Nephrolepis_cordifolia"
[497] "Nephrolepis_hirsutula" "Humata_pyxidata" "Arthropteris_beckleri" "Arthropteris_palisotii"
[501] "Arthropteris_orientalis" "Arthropteris_monocarpa" "Tectaria_devexa" "Tectaria_yunnanensis"
[505] "Tectaria_decurrens" "Tectaria_incisa" "Tectaria_gaudichaudii" "Tectaria_zeylanica"
[509] "Tectaria_impressa" "Polypodium_australe" "Polypodium_pellucidum" "Polypodium_vulgare"
[513] "Polypodium_virginianum" "Campyloneurum_brevifolium" "Microgramma_lycopodioides" "Microgramma_squamulosa"
[517] "Grammitis_baldwinii" "Grammitis_diminuta" "Grammitis_billardierei" "Ctenopteris_heterophylla"
[521] "Grammitis_tenella" "Adenophorus_pinnatifidus" "Adenophorus_abietinus" "Adenophorus_tamariscinus"
[525] "Platycerium_alcicorne" "Platycerium_stemaria" "Pyrrosia_adnascens" "Pyrrosia_eleagnifolia"
[529] "Pyrrosia_serpens" "Pleopeltis_polypodioides" "Serpocaulon_triseriale" "Bolbitis_heteroclita"
[533] "Colysis_hemitoma" "Colysis_pentaphylla" "Microsorum_punctatum" "Phymatosorus_scolopendria"
[537] "Neocheiropteris_palmatopedata" "Neocheiropteris_ovata" "Lepisorus_scolopendrius" "Lepisorus_macrosphaerus"
[541] "Tricholepidium_maculosum" "Lepisorus_thunbergianus" "Lepisorus_contortus" "Rumohra_adiantiformis"
[545] "Lastreopsis_decomposita" "Lastreopsis_hispida" "Elaphoglossum_peltatum" "Elaphoglossum_hybridum"
[549] "Elaphoglossum_aubertii" "Elaphoglossum_piloselloides" "Elaphoglossum_acrostichoides" "Elaphoglossum_petiolatum"
[553] "Elaphoglossum_marojejyense" "Elaphoglossum_latifolium" "Elaphoglossum_herminieri" "Ctenitis_rhodolepis"
[557] "Cyrtomium_fortunei" "Polystichum_tripteron" "Polystichum_dielsii" "Polystichum_luctuosum"
[561] "Polystichum_tsus" "Polystichum_aculeatum" "Polystichum_setiferum" "Polystichum_pycnopterum"
[565] "Polystichum_eximium" "Polystichum_neozelandicum" "Polystichum_yunnanense" "Polystichum_chilense"
[569] "Polystichum_munitum" "Polystichum_acrostichoides" "Polystichum_mohrioides" "Polystichum_lonchitis"
[573] "Polystichum_montevidense" "Polybotrya_caudata" "Arachniodes_rhomboidea" "Arachniodes_festina"
[577] "Arachniodes_simplicior" "Arachniodes_globisora" "Arachniodes_sporadosora" "Acrophorus_stipellatus"
[581] "Dryopteris_stenolepis" "Dryopteris_wallichiana" "Dryopteris_lepidopoda" "Dryopteris_oreades"
[585] "Dryopteris_filix" "Dryopteris_marginalis" "Dryopteris_chrysocoma" "Dryopteris_pseudovaria"
[589] "Dryopteris_sublacera" "Dryopteris_cochleata" "Dryopteris_lacera" "Dryopteris_aemula"
[593] "Nothoperanema_squamisetum" "Nothoperanema_hendersonii" "Dryopsis_mariformis" "Dryopteris_setosa"
[597] "Dryopteris_fuscipes" "Dryopteris_erythrosora" "Dryopteris_sparsa" "Dryopteris_juxtaposita"
[601] "Dryopteris_goldiana" "Dryopteris_intermedia" "Dryopteris_dilatata" "Dryopteris_remota"
[605] "Dryopteris_carthusiana" "Dryopteris_expansa" "Dryopteris_cristata" "Ginkgo_biloba"
[609] "Cycas_micholitzii" "Cycas_multipinnata" "Cycas_pectinata" "Cycas_silvestris"
[613] "Cycas_condaoensis" "Cycas_simplicipinna" "Cycas_tanqingii" "Cycas_szechuanensis"
[617] "Cycas_wadei" "Cycas_taiwaniana" "Cycas_taitungensis" "Cycas_revoluta"
[621] "Cycas_furfuracea" "Cycas_hoabinhensis" "Cycas_media" "Cycas_rumphii"
[625] "Cycas_lindstromii" "Cycas_circinalis" "Cycas_seemannii" "Cycas_thouarsii"
[629] "Cycas_zeylanica" "Cycas_edentata" "Cycas_clivicola" "Cycas_siamensis"
[633] "Cycas_pranburiensis" "Cycas_tansachana" "Cycas_chamaoensis" "Cycas_nongnoochiae"
[637] "Cycas_segmentifida" "Cycas_changjiangensis" "Encephalartos_altensteinii" "Dioon_spinulosum"
[641] "Dioon_mejiae" "Dioon_rzedowskii" "Dioon_edule" "Dioon_tomasellii"
[645] "Dioon_purpusii" "Dioon_holmgrenii" "Dioon_merolae" "Dioon_califanoi"
[649] "Dioon_caputoi" "Stangeria_eriopus" "Microcycas_calocoma" "Zamia_poeppigiana"
[653] "Zamia_pumila" "Zamia_vazquezii" "Zamia_restrepoi" "Zamia_furfuracea"
[657] "Ceratozamia_miqueliana" "Ceratozamia_robusta" "Ceratozamia_microstrobila" "Ceratozamia_norstogii"
[661] "Ceratozamia_kuesteriana" "Ceratozamia_mexicana" "Ceratozamia_sabatoi" "Ceratozamia_euryphyllidia"
[665] "Ceratozamia_morettii" "Ceratozamia_alvarezii" "Ceratozamia_hildae" "Bowenia_spectabilis"
[669] "Bowenia_serrulata" "Macrozamia_fraseri" "Macrozamia_miquelii" "Macrozamia_platyrhachis"
[673] "Macrozamia_fawcettii" "Macrozamia_reducta" "Macrozamia_macdonnellii" "Macrozamia_riedlei"
[677] "Macrozamia_glaucophylla" "Macrozamia_polymorpha" "Macrozamia_pauli" "Macrozamia_communis"
[681] "Macrozamia_lucida" "Macrozamia_moorei" "Macrozamia_dyeri" "Lepidozamia_hopei"
[685] "Lepidozamia_peroffskyana" "Encephalartos_tegulaneus" "Encephalartos_septentrionalis" "Encephalartos_bubalinus"
[689] "Encephalartos_kisambo" "Encephalartos_macrostrobilus" "Encephalartos_whitelockii" "Encephalartos_gratus"
[693] "Encephalartos_hildebrandtii" "Encephalartos_dyerianus" "Encephalartos_latifrons" "Encephalartos_transvenosus"
[697] "Encephalartos_munchii" "Encephalartos_pterogonus" "Encephalartos_humilis" "Encephalartos_eugene"
[701] "Encephalartos_lanatus" "Encephalartos_ghellinckii" "Encephalartos_paucidentatus" "Encephalartos_cycadifolius"
[705] "Encephalartos_laevifolius" "Encephalartos_lebomboensis" "Encephalartos_arenarius" "Encephalartos_horridus"
[709] "Encephalartos_cupidus" "Encephalartos_trispinosus" "Encephalartos_inopinus" "Encephalartos_princeps"
[713] "Encephalartos_lehmannii" "Encephalartos_barteri" "Encephalartos_manikensis" "Encephalartos_longifolius"
[717] "Encephalartos_friderici" "Encephalartos_villosus" "Encephalartos_caffer" "Encephalartos_woodii"
[721] "Encephalartos_natalensis" "Encephalartos_ferox" "Encephalartos_ngoyanus" "Encephalartos_senticosus"
[725] "Encephalartos_heenanii" "Encephalartos_umbeluziensis" "Welwitschia_mirabilis" "Gnetum_leyboldii"
[729] "Gnetum_nodiflorum" "Gnetum_urens" "Gnetum_costatum" "Gnetum_gnemon"
[733] "Gnetum_africanum" "Gnetum_gnemonoides" "Gnetum_parvifolium" "Gnetum_montanum"
[737] "Gnetum_cuspidatum" "Gnetum_macrostachyum" "Gnetum_latifolium" "Gnetum_ula"
[741] "Gnetum_neglectum" "Ephedra_tweediana" "Ephedra_frustillata" "Ephedra_chilensis"
[745] "Ephedra_nevadensis" "Ephedra_torreyana" "Ephedra_californica" "Ephedra_aspera"
[749] "Ephedra_trifurca" "Ephedra_viridis" "Ephedra_gerardiana" "Ephedra_equisetina"
[753] "Ephedra_przewalskii" "Ephedra_minuta" "Ephedra_pachyclada" "Ephedra_sinica"
[757] "Ephedra_intermedia" "Ephedra_distachya" "Ephedra_regeliana" "Ephedra_foeminea"
[761] "Ephedra_fragilis" "Ephedra_foliata" "Ephedra_alata" "Ephedra_aphylla"
[765] "Cedrus_libani" "Cedrus_atlantica" "Cedrus_deodara" "Keteleeria_davidiana"
[769] "Pseudolarix_amabilis" "Nothotsuga_longibracteata" "Tsuga_mertensiana" "Tsuga_heterophylla"
[773] "Tsuga_dumosa" "Tsuga_diversifolia" "Tsuga_caroliniana" "Tsuga_sieboldii"
[777] "Tsuga_forrestii" "Tsuga_chinensis" "Keteleeria_evelyniana" "Abies_bracteata"
[781] "Abies_firma" "Abies_fabri" "Abies_holophylla" "Abies_fargesii"
[785] "Abies_fraseri" "Abies_koreana" "Abies_grandis" "Abies_recurvata"
[789] "Abies_nephrolepis" "Abies_kawakamii" "Abies_homolepis" "Abies_procera"
[793] "Abies_amabilis" "Abies_magnifica" "Abies_alba" "Abies_nebrodensis"
[797] "Abies_nordmanniana" "Abies_pinsapo" "Abies_numidica" "Abies_veitchii"
[801] "Abies_cilicica" "Abies_cephalonica" "Pseudotsuga_sinensis" "Pseudotsuga_macrocarpa"
[805] "Larix_gmelinii" "Larix_decidua" "Larix_occidentalis" "Larix_laricina"
[809] "Larix_sibirica" "Larix_kaempferi" "Larix_griffithii" "Larix_mastersiana"
[813] "Larix_potaninii" "Cathaya_argyrophylla" "Picea_sitchensis" "Picea_breweriana"
[817] "Picea_glauca" "Picea_engelmannii" "Picea_pungens" "Picea_chihuahuana"
[821] "Picea_schrenkiana" "Picea_spinulosa" "Picea_likiangensis" "Picea_orientalis"
[825] "Picea_alcoquiana" "Picea_morrisonicola" "Picea_brachytyla" "Picea_purpurea"
[829] "Picea_wilsonii" "Picea_glehnii" "Picea_jezoensis" "Picea_rubens"
[833] "Picea_omorika" "Picea_mariana" "Picea_abies" "Picea_asperata"
[837] "Picea_koyamae" "Picea_obovata" "Picea_meyeri" "Picea_koraiensis"
[841] "Picea_crassifolia" "Picea_torano" "Picea_retroflexa" "Pinus_nelsonii"
[845] "Pinus_balfouriana" "Pinus_aristata" "Pinus_monophylla" "Pinus_quadrifolia"
[849] "Pinus_cembroides" "Pinus_edulis" "Pinus_maximartinezii" "Pinus_parviflora"
[853] "Pinus_fenzeliana" "Pinus_armandii" "Pinus_albicaulis" "Pinus_lambertiana"
[857] "Abies_sibirica" "Pinus_pumila" "Pinus_bhutanica" "Pinus_monticola"
[861] "Pinus_peuce" "Pinus_krempfii" "Pinus_gerardiana" "Pinus_bungeana"
[865] "Pinus_koraiensis" "Pinus_strobus" "Pinus_flexilis" "Pinus_strobiformis"
[869] "Pinus_banksiana" "Pinus_heldreichii" "Pinus_canariensis" "Pinus_brutia"
[873] "Pinus_pinaster" "Pinus_pinea" "Pinus_tropicalis" "Pinus_massoniana"
[877] "Pinus_merkusii" "Pinus_densiflora" "Pinus_taiwanensis" "Pinus_thunbergii"
[881] "Pinus_kesiya" "Pinus_densata" "Pinus_yunnanensis" "Pinus_tabuliformis"
[885] "Pinus_hwangshanensis" "Pinus_luchuensis" "Pinus_clausa" "Pinus_virginiana"
[889] "Pinus_arizonica" "Pinus_douglasiana" "Pinus_coulteri" "Pinus_engelmannii"
[893] "Pinus_jeffreyi" "Pinus_devoniana" "Pinus_hartwegii" "Pinus_montezumae"
[897] "Pinus_sabiniana" "Pinus_ayacahuite" "Pinus_ponderosa" "Pinus_torreyana"
[901] "Pinus_taeda" "Pinus_pungens" "Pinus_serotina" "Pinus_muricata"
[905] "Pinus_attenuata" "Pinus_glabra" "Pinus_teocote" "Pinus_leiophylla"
[909] "Pinus_greggii" "Pinus_lumholtzii" "Pinus_lawsonii" "Pinus_herrerae"
[913] "Pinus_pringlei" "Pinus_oocarpa" "Pinus_cubensis" "Pinus_palustris"
[917] "Pinus_caribaea" "Pinus_elliottii" "Pinus_echinata" "Sciadopitys_verticillata"
[921] "Cephalotaxus_harringtonia" "Cephalotaxus_fortunei" "Cephalotaxus_harringtonii" "Amentotaxus_yunnanensis"
[925] "Amentotaxus_argotaenia" "Torreya_grandis" "Torreya_californica" "Torreya_taxifolia"
[929] "Torreya_nucifera" "Austrotaxus_spicata" "Pseudotaxus_chienii" "Taxus_floridana"
[933] "Taxus_globosa" "Taxus_brevifolia" "Taxus_x" "Taxus_wallichiana"
[937] "Taxus_canadensis" "Taxus_cuspidata" "Cunninghamia_lanceolata" "Taiwania_cryptomerioides"
[941] "Metasequoia_glyptostroboides" "Sequoiadendron_giganteum" "Sequoia_sempervirens" "Athrotaxis_laxifolia"
[945] "Athrotaxis_selaginoides" "Athrotaxis_cupressoides" "Cryptomeria_japonica" "Glyptostrobus_pensilis"
[949] "Taxodium_huegelii" "Taxodium_distichum" "Papuacedrus_papuana" "Austrocedrus_chilensis"
[953] "Libocedrus_plumosa" "Pilgerodendron_uviferum" "Libocedrus_yateensis" "Libocedrus_bidwillii"
[957] "Diselma_archeri" "Fitzroya_cupressoides" "Widdringtonia_wallichii" "Widdringtonia_nodiflora"
[961] "Widdringtonia_schwarzii" "Callitris_preissii" "Actinostrobus_pyramidalis" "Actinostrobus_acuminatus"
[965] "Callitris_endlicheri" "Callitris_rhomboidea" "Thujopsis_dolabrata" "Thuja_koraiensis"
[969] "Thuja_standishii" "Thuja_plicata" "Chamaecyparis_obtusa" "Chamaecyparis_lawsoniana"
[973] "Fokienia_hodginsii" "Chamaecyparis_thyoides" "Chamaecyparis_formosensis" "Chamaecyparis_pisifera"
[977] "Tetraclinis_articulata" "Platycladus_orientalis" "Microbiota_decussata" "Calocedrus_decurrens"
[981] "Calocedrus_macrolepis" "Calocedrus_formosana" "Cupressus_funebris" "Cupressus_sempervirens"
[985] "Cupressus_chengiana" "Cupressus_dupreziana" "Cupressus_gigantea" "Cupressus_duclouxiana"
[989] "Cupressus_cashmeriana" "Cupressus_torulosa" "Cupressus_nootkatensis" "Cupressus_bakeri"
[993] "Cupressus_arizonica" "Cupressus_lusitanica" "Cupressus_guadalupensis" "Cupressus_macnabiana"
[997] "Cupressus_macrocarpa" "Cupressus_sargentii" "Cupressus_goveniana" "Juniperus_cedrus"
[ reached getOption("max.print") -- omitted 29499 entries ]
Bayesian update functions:
## Test 3 different functions.
## 1. Step function with 3 parameters:
.updateStepFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
hr <- 0
pars.new$theta <- ifelse(midbins>pars.new$beta_center, pars.new$beta_right, pars.new$beta_left)
return(list(pars=pars.new, hr=hr))
}
## 2. Sigmoid function with 5 parameters
.updateSigmoidFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
hr <- 0
pars.new$theta <- pars.new$beta_left + pars.new$beta_right/(1 + exp(pars.new$beta_slope * (midbins - pars.new$beta_center)))
return(list(pars=pars.new, hr=hr))
}
## 3. Linear function with 2 parameters
.updateLinearFxTheta <- function(pars, cache, d, ct, move, prior=NULL){
pars.new <- bayou:::.slidingWindowProposal(cache, pars, d, move, ct)$pars
hr <- 0
pars.new$theta <- pars.new$beta_intercept + pars.new$beta_slope * midbins
return(list(pars=pars.new, hr=hr))
}
Bayesian update functions for dealing with simmap reconstructions
## Other needed functions
.newMap <- function(pars, cache, d, ct, prior=NULL, move=NULL){
pars.new <- pars
stree <- reorderSimmap(make.simmap(cache$phy, setNames(cache$pred[[1]], cache$phy$tip.label), Q=Q, message=FALSE), "postorder")
shifts <- sapply(stree$maps, function(x) names(x)[-1])
pars.new$sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
pars.new$t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
pars.new$loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
pars.new$k <- length(pars.new$sb)
hr <- 0
return(list(pars=pars.new, hr=Inf))
}
.newMapPresample <- function(pars, cache, d, ct, prior=NULL, move=NULL){
pars.new <- pars
index <- sample(1:length(strees.bayou), 1, replace=FALSE)
pars.new$sb <- strees.bayou[[index]]$sb #unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
pars.new$t2 <- strees.bayou[[index]]$t2 #as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
pars.new$loc <- unname(strees.bayou[[index]]$loc) #unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
pars.new$k <- length(pars.new$sb)
hr <- 0
return(list(pars=pars.new, hr=Inf))
}
## Empty functions to replace the deterministically specified theta values
dnull <- function(x, log=TRUE){
if(log) 0 else 1
}
rnull <- function(x){
return(x)
}
Place the predictor into bins and transform the tree to unit height.
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])
td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat
Fit a meristic model to the binned predictor data and construct a set of stochastic maps.
## Use diversitree to fit a meristic model:
mkn.lik <- make.mkn.meristic(td$phy, td[['binpred']], k = ncat)
mkn.lik <- constrain(mkn.lik, q.up~q.down)
p <- c(0.1)
fit.mkn <- find.mle(mkn.lik, p)
Q <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
Q[i, i+1] <- fit.mkn$par[1]
Q[i+1, i] <- fit.mkn$par[1]
}
diag(Q) <- apply(Q, 1, sum)*-1
rownames(Q) <- colnames(Q) <- 1:ncol(Q)
simmap2bayou <- function(stree){
shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
td <- reorder(td, "postorder")
stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8
1 -9.886328 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000
4 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000
6 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000
7 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328
8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656
9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328
9
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000000
6 0.000000
7 0.000000
8 9.886328
9 -9.886328
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
9
0.1111111
Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.
strees <- make.simmap(td$phy, td[['binpred']], Q=Q, nsim=100)
make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8
1 -9.886328 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000 0.000000
4 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000 0.000000
6 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328 0.000000
7 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656 9.886328
8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328 -19.772656
9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 9.886328
9
1 0.000000
2 0.000000
3 0.000000
4 0.000000
5 0.000000
6 0.000000
7 0.000000
8 9.886328
9 -9.886328
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
9
0.1111111
Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.
for(i in 1:length(strees)) strees[[i]] <- reorderSimmap(strees[[i]], "postorder")
strees.bayou <- lapply(strees, simmap2bayou)
plotSimmap(stree, col=setNames(cm.colors(ncat), 1:ncat))
model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik
[,1]
[1,] -439.3852
shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unname(unlist(sapply(stree$maps, function(x) cumsum(x)[-1])))
tmp <- lm(td[[trait]]~td[[pred]]+I(td[[pred]]^2)+I(td[[pred]]^3))
strees.bayou <- readRDS("../output/OUwie/strees.bayou.rds")
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.step <- list(alpha=1, sig2=0.5,
beta_left=step.starts[2],
beta_right=step.starts[3],
beta_center=step.starts[1],
k=length(sb), ntheta=ncat-1,
theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.step$theta)
sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoid <- list(alpha=1, sig2=0.5,
beta_left=sigmoid.starts$beta_left,
beta_right=sigmoid.starts$beta_right,
beta_center=sigmoid.starts$beta_center,
beta_slope = sigmoid.starts$beta_slope,
k=length(sb), ntheta=ncat-1,
theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.sigmoid$theta)
linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linear <- list(alpha=1, sig2=0.5,
beta_intercept=linear.starts$beta_intercept,
beta_slope=linear.starts$beta_slope,
k=length(sb), ntheta=ncat-1,
theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
sb=sb, loc=loc, t2=t2)
plot(midbins, startpar.linear$theta)
## Set priors
prior.step <- make.prior(td$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
prior.sigmoid <- make.prior(td$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dbeta_slope="dlnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dbeta_slope=list(meanlog=-3, sdlog=1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
prior.linear <- make.prior(td$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_intercept="dnorm",
dbeta_slope="dnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_intercept=list(mean=-6, sd=1.5),
dbeta_slope=list(mean=0, sd=0.1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=list(k=length(sb), ntheta=ncat-1, sb=sb, loc=loc, t2=t2)
)
model.stepTheta <- list(moves = list(alpha=".multiplierProposal",
sig2=".multiplierProposal",
beta_left=".updateStepFxTheta",
beta_right=".updateStepFxTheta",
beta_center=".updateStepFxTheta",
theta=".newMapPresample"),
control.weights = list(alpha=10, sig2=10,
beta_left=10,
beta_right=10,
beta_center=10,
theta=1,
k=0),
D = list(alpha=1, sig2=1,
beta_left=0.2,
beta_right=0.2,
beta_center=20,
theta = 1),
parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "theta", "k", "ntheta"),
rjpars = c(),
shiftpars = c("sb", "loc", "t2"),
lik.fn = bayou.lik)
model.stepTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "k") #The parameters we want to print
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
model.sigmoidTheta <- list(moves = list(alpha=".multiplierProposal",
sig2=".multiplierProposal",
beta_left=".updateSigmoidFxTheta",
beta_right=".updateSigmoidFxTheta",
beta_center=".updateSigmoidFxTheta",
beta_slope=".updateSigmoidFxTheta",
theta=".newMapPresample"),
control.weights = list(alpha=10, sig2=10,
beta_left=10,
beta_right=10,
beta_center=10,
beta_slope=10,
theta=1,
k=0),
D = list(alpha=1, sig2=1,
beta_left=0.2,
beta_right=0.2,
beta_center=20,
beta_slope=0.005,
theta = 1),
parorder = c("alpha", "sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "theta", "k", "ntheta"),
rjpars = c(),
shiftpars = c("sb", "loc", "t2"),
lik.fn = bayou.lik)
model.sigmoidTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_left", "beta_right", "beta_center", "beta_slope", "k") #The parameters we want to print
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_left, pars$beta_right, pars$beta_center, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
model.linearTheta <- list(moves = list(alpha=".multiplierProposal",
sig2=".multiplierProposal",
beta_intercept=".updateLinearFxTheta",
beta_slope=".updateLinearFxTheta",
theta=".newMapPresample"),
control.weights = list(alpha=10, sig2=10,
beta_intercept=10,
beta_slope=10,
theta=1,
k=0),
D = list(alpha=1, sig2=1,
beta_intercept=0.5,
beta_slope=0.005,
theta = 1),
parorder = c("alpha", "sig2", "beta_intercept", "beta_slope", "theta", "k", "ntheta"),
rjpars = c(),
shiftpars = c("sb", "loc", "t2"),
lik.fn = bayou.lik)
model.linearTheta$monitor.fn <- function(i, lik, pr, pars, accept, accept.type, j){
names <- c("gen", "lnL", "prior", "alpha","sig2", "beta_intercept", "beta_slope", "k") #The parameters we want to print
string <- "%-8i%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8.2f%-8i" #The types of each parameter
acceptratios <- tapply(accept, accept.type, mean)
names <- c(names, names(acceptratios))
if(j==0){
cat(sprintf("%-7.7s", names), "\n", sep=" ")
}
cat(sprintf(string, i, lik, pr, pars$alpha, pars$sig2[1], pars$beta_intercept, pars$beta_slope, pars$k), sprintf("%-8.2f", acceptratios),"\n", sep="")
}
## Just a bunch of tests
cache <- bayou:::.prepare.ou.univariate(td$phy, td[['lnVs']], SE=0, pred=td['binpred'])
prior.step(startpar.step)
[1] -13.09416
model.stepTheta$lik.fn(startpar.step, cache, cache$dat)$loglik
[,1]
[1,] -24543.32
ct <- bayou:::.buildControl(startpar.step, prior.step, move.weights=model.stepTheta$control.weights)
pars.new <- .newMapPresample(startpar.step, cache, d=model.stepTheta$D$theta, ct = ct)$pars
prior.step(pars.new)
[1] -13.09416
model.stepTheta$lik.fn(pars.new, cache, cache$dat)$loglik
[,1]
[1,] -24558.23
pp <- pars.new
prior.step(pp)
[1] -13.09416
model.stepTheta$lik.fn(pp, cache, cache$dat)$loglik
[,1]
[1,] -24558.23
new.pp <- .newMapPresample(pp, cache, 1, ct)$pars
model.stepTheta$lik.fn(new.pp, cache, cache$dat)$loglik
[,1]
[1,] -24518.64
prior.linear(startpar.linear)
[1] -2.994012
new.pp <- .newMapPresample(startpar.linear, cache, 1, ct)$pars
model.linearTheta$lik.fn(new.pp, cache, cache$dat)$loglik
[,1]
[1,] -24590.84
mymcmc.step <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.stepTheta, prior=prior.step, startpar=startpar.step, new.dir=TRUE, outname="stepTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.sigmoid <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoid, startpar=startpar.sigmoid, new.dir=TRUE, outname="sigmoidTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.linear <- bayou.makeMCMC(td$phy, td[[trait]], pred=td['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linear, startpar=startpar.linear, new.dir=TRUE, outname="linearTheta_r2", plot.freq=NULL, ticker.freq=2000, samp = 100)
mymcmc.step$run(100000)
gen lnL prior alpha sig2 beta_le beta_ri beta_ce k .alpha .beta_c .beta_l .beta_r .sig2 .theta
2000 -590.84 -38.20 123.17 440.79 -6.55 -4.65 13.25 896 0.31 0.93 0.78 0.79 0.25 1.00
4000 -592.19 -37.47 107.69 420.95 -6.31 -4.69 27.22 892 0.29 0.93 0.79 0.80 0.23 1.00
6000 -583.48 -38.62 132.64 454.67 -6.53 -4.72 23.81 877 0.27 0.93 0.80 0.81 0.24 1.00
8000 -589.63 -39.31 147.54 493.52 -6.26 -4.78 0.09 892 0.27 0.93 0.79 0.82 0.23 1.00
10000 -588.58 -38.97 133.40 533.08 -6.32 -4.89 32.15 826 0.27 0.93 0.79 0.82 0.23 1.00
12000 -583.27 -40.78 175.22 664.15 -6.36 -4.84 -13.44 852 0.26 0.93 0.78 0.82 0.23 1.00
14000 -597.02 -41.23 189.28 677.93 -6.13 -4.62 62.50 894 0.26 0.93 0.78 0.83 0.23 1.00
16000 -588.78 -38.49 133.22 420.87 -6.21 -4.11 97.16 855 0.27 0.93 0.78 0.83 0.23 1.00
18000 -584.75 -39.28 143.07 522.83 -6.41 -4.52 1.94 874 0.26 0.92 0.79 0.83 0.24 1.00
20000 -583.40 -41.06 187.33 642.00 -6.57 -4.67 13.89 867 0.26 0.93 0.79 0.83 0.23 1.00
22000 -597.02 -39.85 150.75 612.46 -6.52 -4.87 -0.83 938 0.26 0.93 0.79 0.83 0.23 1.00
24000 -592.27 -36.09 91.15 308.97 -6.29 -4.87 -19.24 864 0.26 0.93 0.79 0.83 0.23 1.00
26000 -594.73 -37.87 119.58 400.65 -6.42 -4.87 24.79 869 0.25 0.93 0.79 0.83 0.23 1.00
28000 -583.76 -37.74 115.39 408.31 -6.46 -4.76 28.95 849 0.25 0.92 0.79 0.83 0.23 1.00
30000 -584.13 -39.36 145.15 526.49 -6.64 -4.80 28.89 852 0.25 0.92 0.79 0.83 0.23 1.00
32000 -582.61 -39.04 142.30 470.36 -6.52 -4.71 33.03 836 0.25 0.92 0.79 0.83 0.23 1.00
34000 -592.11 -38.49 129.83 446.70 -6.43 -4.84 -29.96 901 0.25 0.92 0.79 0.83 0.23 1.00
36000 -612.74 -34.93 75.46 263.14 -6.29 -4.79 -4.08 853 0.25 0.92 0.79 0.83 0.24 1.00
38000 -591.83 -37.88 116.14 432.02 -6.48 -4.60 28.81 873 0.25 0.92 0.79 0.83 0.24 1.00
40000 -603.99 -39.44 146.89 532.45 -6.39 -4.93 -6.71 861 0.25 0.92 0.79 0.83 0.24 1.00
42000 -586.39 -40.05 161.06 570.30 -6.33 -4.94 16.44 855 0.25 0.92 0.79 0.83 0.24 1.00
44000 -583.98 -38.96 137.79 490.19 -6.57 -4.86 5.83 826 0.25 0.93 0.79 0.82 0.24 1.00
46000 -584.42 -39.59 149.11 551.60 -6.48 -4.84 1.37 826 0.25 0.93 0.79 0.82 0.24 1.00
48000 -597.49 -37.67 111.92 424.51 -6.28 -4.83 -7.83 878 0.25 0.93 0.79 0.82 0.24 1.00
50000 -606.15 -39.68 152.47 545.14 -6.54 -4.73 26.50 853 0.25 0.93 0.79 0.82 0.24 1.00
52000 -583.56 -39.35 148.20 496.32 -6.55 -4.86 26.14 826 0.25 0.93 0.79 0.82 0.24 1.00
54000 -580.89 -42.45 231.74 728.12 -6.53 -4.70 14.94 925 0.25 0.93 0.79 0.82 0.24 1.00
56000 -599.03 -38.91 133.52 515.63 -6.14 -4.79 -19.08 857 0.25 0.93 0.79 0.82 0.24 1.00
58000 -581.65 -40.03 161.01 565.71 -6.43 -4.79 -25.00 925 0.25 0.93 0.79 0.83 0.24 1.00
60000 -576.63 -42.11 220.94 698.27 -6.63 -4.79 -7.62 859 0.25 0.93 0.79 0.82 0.24 1.00
62000 -590.18 -40.45 173.03 580.82 -6.38 -4.79 27.79 868 0.25 0.93 0.79 0.83 0.24 1.00
64000 -594.47 -36.24 89.12 351.08 -6.56 -4.87 -3.13 892 0.25 0.93 0.79 0.83 0.24 1.00
66000 -590.92 -46.53 394.08 1229.26 -6.61 -4.76 17.19 861 0.25 0.93 0.79 0.83 0.24 1.00
68000 -588.47 -37.45 108.85 406.31 -6.62 -4.53 -11.20 826 0.25 0.93 0.79 0.83 0.24 1.00
70000 -589.92 -40.52 168.51 645.45 -6.48 -4.68 -16.62 804 0.25 0.93 0.79 0.82 0.24 1.00
72000 -592.50 -42.38 223.14 777.90 -6.38 -5.03 15.14 865 0.25 0.93 0.79 0.82 0.24 1.00
74000 -590.80 -40.73 174.08 656.37 -6.56 -4.68 33.71 838 0.26 0.93 0.79 0.82 0.24 1.00
76000 -591.83 -38.55 125.51 500.89 -6.43 -4.78 22.66 834 0.25 0.93 0.79 0.82 0.24 1.00
78000 -590.42 -38.57 130.62 459.82 -6.51 -4.79 15.19 838 0.25 0.93 0.79 0.82 0.24 1.00
80000 -583.16 -39.71 157.24 513.15 -6.48 -4.56 33.58 834 0.26 0.93 0.79 0.82 0.24 1.00
82000 -582.41 -39.94 165.13 508.46 -6.57 -4.78 5.07 906 0.26 0.93 0.79 0.82 0.24 1.00
84000 -593.76 -37.34 111.31 363.99 -6.48 -4.96 25.00 838 0.26 0.93 0.79 0.82 0.24 1.00
86000 -588.90 -41.11 186.94 659.90 -6.49 -4.80 28.37 892 0.26 0.93 0.79 0.82 0.24 1.00
88000 -580.48 -42.04 219.29 689.53 -6.45 -4.66 1.63 906 0.26 0.93 0.79 0.82 0.24 1.00
90000 -591.94 -38.19 120.61 460.06 -6.26 -4.70 13.72 892 0.25 0.93 0.79 0.82 0.24 1.00
92000 -593.83 -38.56 129.95 462.93 -6.47 -4.67 7.51 852 0.25 0.93 0.79 0.82 0.24 1.00
94000 -590.57 -39.48 145.92 551.72 -6.45 -4.69 -3.98 865 0.25 0.93 0.79 0.82 0.24 1.00
96000 -589.16 -40.19 167.17 558.78 -6.47 -4.80 -13.93 866 0.26 0.93 0.79 0.82 0.24 1.00
98000 -591.75 -38.85 130.63 527.34 -6.54 -4.82 0.31 859 0.26 0.93 0.79 0.82 0.24 1.00
100000 -592.66 -38.85 136.69 472.15 -6.39 -4.67 -21.49 859 0.25 0.93 0.79 0.82 0.24 1.00
chain.step <- mymcmc.step$load()
chain.step <- set.burnin(chain.step, 0.3)
plot(chain.step)
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.step$gen), 10), function(i) lines(x, ifelse(x>chain.step$beta_center[i], chain.step$beta_right[i], chain.step$beta_left[i]), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
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mymcmc.sigmoid$run(100000)
gen lnL prior alpha sig2 beta_le beta_ri beta_ce beta_sl k .alpha .beta_c .beta_l .beta_r .beta_s .sig2 .theta
2000 -579.39 -38.65 182.95 611.04 -2.13 -5.92 42.53 0.01 846 0.35 0.72 0.79 0.81 0.49 0.28 1.00
4000 -557.49 -44.44 394.12 1199.24 -2.34 -6.12 17.40 0.01 807 0.31 0.75 0.75 0.82 0.40 0.25 1.00
6000 -570.73 -38.65 184.94 590.22 -2.10 -5.73 61.63 0.01 896 0.30 0.76 0.73 0.82 0.38 0.24 1.00
8000 -577.90 -39.54 214.36 686.73 -2.09 -5.19 85.80 0.01 846 0.28 0.75 0.72 0.81 0.39 0.25 1.00
10000 -566.00 -43.96 371.12 1133.71 -2.59 -5.37 31.63 0.01 935 0.27 0.75 0.73 0.82 0.40 0.25 1.00
12000 -570.57 -40.50 244.09 787.57 -3.18 -4.29 20.82 0.01 841 0.27 0.75 0.72 0.82 0.41 0.25 1.00
14000 -572.54 -38.93 202.08 584.34 -3.44 -4.66 -29.00 0.01 895 0.27 0.74 0.72 0.82 0.43 0.24 1.00
16000 -572.50 -38.42 184.63 548.14 -2.97 -5.07 -5.70 0.01 839 0.26 0.74 0.71 0.82 0.44 0.24 1.00
18000 -570.38 -41.24 250.40 800.82 -2.42 -5.68 57.67 0.01 852 0.26 0.74 0.71 0.82 0.44 0.24 1.00
20000 -568.21 -40.75 246.81 745.01 -2.47 -6.15 -4.04 0.01 925 0.26 0.73 0.71 0.82 0.43 0.24 1.00
22000 -568.20 -36.89 146.95 442.62 -1.99 -6.15 55.13 0.01 867 0.25 0.73 0.70 0.82 0.42 0.25 1.00
24000 -565.80 -40.40 245.53 750.51 -2.67 -5.09 44.04 0.01 855 0.25 0.73 0.71 0.82 0.42 0.24 1.00
26000 -571.11 -43.28 337.83 1181.64 -2.85 -4.92 28.75 0.01 851 0.25 0.74 0.71 0.82 0.42 0.24 1.00
28000 -557.80 -42.24 321.34 860.07 -3.35 -4.87 -22.20 0.01 807 0.26 0.74 0.71 0.82 0.42 0.24 1.00
30000 -557.81 -43.16 374.55 903.59 -2.82 -4.41 61.17 0.01 878 0.26 0.74 0.71 0.82 0.43 0.24 1.00
32000 -572.32 -41.79 285.87 951.22 -2.68 -4.69 48.10 0.01 857 0.26 0.74 0.71 0.82 0.44 0.24 1.00
34000 -567.34 -40.93 258.66 850.16 -3.66 -3.98 -10.38 0.01 887 0.26 0.74 0.71 0.82 0.45 0.24 1.00
36000 -568.31 -41.80 295.74 952.27 -3.82 -3.50 13.83 0.01 826 0.26 0.74 0.71 0.82 0.46 0.24 1.00
38000 -562.73 -37.58 172.88 500.52 -4.21 -3.05 -9.93 0.02 906 0.26 0.74 0.71 0.82 0.47 0.24 1.00
40000 -569.20 -39.07 210.42 633.06 -3.93 -2.83 29.50 0.02 895 0.26 0.74 0.71 0.82 0.49 0.24 1.00
42000 -574.72 -40.30 243.84 783.93 -4.00 -3.03 10.00 0.02 846 0.26 0.74 0.71 0.82 0.50 0.24 1.00
44000 -576.46 -34.75 110.14 367.11 -4.58 -3.19 -52.58 0.02 849 0.26 0.74 0.72 0.82 0.52 0.24 1.00
46000 -579.26 -42.01 304.42 976.10 -4.33 -2.40 20.00 0.02 869 0.26 0.74 0.72 0.82 0.53 0.24 1.00
48000 -570.93 -41.25 279.67 847.94 -3.88 -3.19 -2.91 0.01 866 0.26 0.74 0.72 0.82 0.54 0.25 1.00
50000 -569.75 -39.11 199.65 624.50 -3.06 -4.78 11.91 0.01 868 0.25 0.74 0.72 0.82 0.54 0.25 1.00
52000 -571.94 -43.49 364.57 1114.82 -3.54 -3.86 21.37 0.01 866 0.25 0.74 0.71 0.82 0.54 0.24 1.00
54000 -574.76 -38.59 187.01 618.30 -2.94 -4.78 8.49 0.01 851 0.25 0.74 0.71 0.82 0.54 0.25 1.00
56000 -579.37 -37.03 143.30 538.58 -3.62 -4.70 -49.23 0.01 854 0.25 0.75 0.72 0.82 0.54 0.25 1.00
58000 -565.39 -39.86 232.52 673.89 -3.40 -3.94 29.58 0.01 836 0.25 0.74 0.72 0.82 0.54 0.25 1.00
60000 -563.78 -39.90 236.18 631.31 -2.88 -5.03 26.07 0.01 873 0.25 0.74 0.72 0.82 0.54 0.25 1.00
62000 -555.36 -41.65 303.76 825.78 -3.59 -3.37 31.83 0.01 836 0.25 0.74 0.72 0.82 0.54 0.25 1.00
64000 -566.36 -38.48 191.88 545.73 -3.71 -4.06 -14.18 0.01 874 0.25 0.74 0.72 0.82 0.55 0.25 1.00
66000 -578.59 -38.96 196.95 677.28 -3.58 -3.62 24.24 0.01 869 0.25 0.74 0.72 0.82 0.55 0.25 1.00
68000 -579.21 -37.27 165.59 480.62 -4.45 -2.57 15.60 0.02 825 0.25 0.74 0.72 0.82 0.56 0.25 1.00
70000 -571.85 -41.43 282.85 910.70 -4.37 -2.75 -9.26 0.02 838 0.25 0.75 0.72 0.82 0.56 0.24 1.00
72000 -570.63 -39.79 229.36 654.21 -3.52 -3.95 13.79 0.01 810 0.25 0.75 0.72 0.82 0.57 0.24 1.00
74000 -558.05 -37.51 172.64 486.45 -3.60 -3.58 25.53 0.02 836 0.25 0.74 0.72 0.82 0.57 0.24 1.00
76000 -568.03 -36.78 153.02 455.04 -3.77 -3.57 5.19 0.01 879 0.25 0.74 0.72 0.82 0.57 0.24 1.00
78000 -569.78 -40.68 248.48 842.61 -3.78 -3.64 14.95 0.01 834 0.25 0.74 0.72 0.82 0.57 0.25 1.00
80000 -573.10 -39.64 228.50 673.36 -4.25 -2.86 -14.10 0.02 879 0.25 0.74 0.72 0.82 0.58 0.25 1.00
82000 -559.90 -38.89 204.63 602.05 -4.01 -3.61 -21.88 0.01 818 0.25 0.74 0.72 0.82 0.59 0.25 1.00
84000 -569.71 -37.99 179.09 565.55 -4.38 -2.75 -5.22 0.02 925 0.25 0.74 0.72 0.82 0.59 0.25 1.00
86000 -555.41 -37.12 156.58 509.35 -4.10 -3.57 -27.69 0.01 788 0.25 0.74 0.72 0.82 0.59 0.24 1.00
88000 -567.70 -40.01 242.16 675.87 -3.98 -3.18 4.88 0.01 925 0.25 0.75 0.72 0.82 0.60 0.24 1.00
90000 -578.71 -41.73 280.42 954.27 -3.16 -4.54 7.05 0.01 859 0.25 0.75 0.72 0.82 0.60 0.25 1.00
92000 -566.31 -43.36 347.88 1224.21 -3.53 -4.02 3.51 0.01 835 0.25 0.74 0.72 0.82 0.60 0.25 1.00
94000 -574.04 -40.55 250.45 742.14 -3.11 -4.13 49.77 0.01 897 0.25 0.75 0.72 0.82 0.60 0.25 1.00
96000 -578.37 -38.35 180.00 578.34 -3.83 -4.68 -76.81 0.01 882 0.25 0.75 0.72 0.82 0.60 0.25 1.00
98000 -568.41 -41.33 276.13 822.04 -2.95 -4.92 16.90 0.01 810 0.25 0.75 0.72 0.82 0.60 0.25 1.00
100000 -573.86 -45.99 488.74 1490.31 -2.78 -4.53 73.80 0.01 875 0.25 0.75 0.72 0.82 0.59 0.25 1.00
chain.sigmoid <- mymcmc.sigmoid$load()
chain.sigmoid <- set.burnin(chain.sigmoid, 0.3)
plot(chain.sigmoid)
plot(midbins, startpar.step$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.sigmoid$gen), 1), function(i) lines(x, chain.sigmoid$beta_left[i]+chain.sigmoid$beta_right[i]/(1 + exp(chain.sigmoid$beta_slope[i]*(x-chain.sigmoid$beta_center[i]))), col=makeTransparent(rgb(1, 0, 0), alpha=10)))
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mymcmc.linear$run(100000)
gen lnL prior alpha sig2 beta_in beta_sl k .alpha .beta_i .beta_s .sig2 .theta
2000 -572.10 -29.46 158.75 506.01 -5.53 0.01 811 0.35 0.50 0.47 0.26 1.00
4000 -570.47 -37.62 481.10 1325.13 -5.50 0.01 858 0.30 0.45 0.46 0.25 1.00
6000 -576.73 -30.28 173.30 615.18 -5.65 0.01 859 0.27 0.43 0.44 0.24 1.00
8000 -572.35 -31.74 212.98 743.03 -5.66 0.01 866 0.26 0.43 0.44 0.25 1.00
10000 -560.90 -33.58 282.48 839.97 -5.45 0.01 863 0.26 0.43 0.44 0.25 1.00
12000 -571.84 -36.49 415.77 1181.50 -5.54 0.01 838 0.25 0.43 0.43 0.25 1.00
14000 -561.23 -31.69 217.92 677.45 -5.58 0.01 851 0.25 0.43 0.42 0.25 1.00
16000 -573.89 -29.52 157.71 536.20 -5.64 0.01 896 0.25 0.43 0.42 0.24 1.00
18000 -569.20 -27.58 117.45 412.34 -5.50 0.01 849 0.26 0.43 0.42 0.24 1.00
20000 -573.08 -31.78 213.79 753.12 -5.66 0.01 804 0.26 0.43 0.42 0.25 1.00
22000 -577.22 -30.56 185.56 585.96 -5.50 0.01 866 0.26 0.43 0.42 0.25 1.00
24000 -562.03 -29.33 156.51 490.26 -5.52 0.01 818 0.26 0.44 0.42 0.24 1.00
26000 -567.11 -30.92 202.55 561.21 -5.59 0.01 877 0.26 0.43 0.42 0.25 1.00
28000 -578.43 -30.63 189.43 574.16 -5.51 0.01 867 0.26 0.43 0.42 0.24 1.00
30000 -568.75 -32.85 263.18 725.16 -5.62 0.01 866 0.26 0.43 0.42 0.25 1.00
32000 -571.49 -35.74 367.99 1170.33 -5.57 0.01 876 0.26 0.43 0.41 0.25 1.00
34000 -573.84 -28.03 137.66 351.82 -5.50 0.01 867 0.26 0.43 0.41 0.25 1.00
36000 -575.74 -34.09 296.30 962.45 -5.60 0.01 868 0.26 0.43 0.41 0.25 1.00
38000 -571.84 -31.04 202.19 597.22 -5.55 0.01 858 0.26 0.43 0.41 0.25 1.00
40000 -577.03 -31.61 207.97 741.22 -5.63 0.01 866 0.26 0.43 0.41 0.25 1.00
42000 -579.65 -28.25 131.47 436.53 -5.42 0.01 875 0.26 0.43 0.41 0.25 1.00
44000 -574.60 -32.84 243.39 891.08 -5.56 0.01 878 0.26 0.43 0.41 0.25 1.00
46000 -565.61 -33.11 263.26 810.82 -5.46 0.01 874 0.26 0.43 0.41 0.25 1.00
48000 -581.27 -29.16 156.16 462.74 -5.79 0.01 871 0.26 0.43 0.41 0.25 1.00
50000 -570.69 -29.89 172.00 515.81 -5.61 0.01 925 0.25 0.43 0.41 0.25 1.00
52000 -578.44 -31.12 194.53 676.49 -5.42 0.01 897 0.26 0.43 0.41 0.25 1.00
54000 -570.78 -29.75 166.21 522.91 -5.57 0.01 925 0.25 0.43 0.41 0.25 1.00
56000 -576.91 -32.46 246.74 707.35 -5.53 0.01 861 0.25 0.43 0.41 0.25 1.00
58000 -574.94 -30.98 196.68 627.54 -5.59 0.01 868 0.26 0.43 0.41 0.25 1.00
60000 -573.95 -30.97 198.70 608.13 -5.64 0.01 838 0.26 0.43 0.41 0.25 1.00
62000 -569.48 -33.97 292.57 937.03 -5.58 0.01 811 0.26 0.43 0.41 0.25 1.00
64000 -584.18 -30.13 179.96 519.31 -5.68 0.01 869 0.25 0.43 0.41 0.25 1.00
66000 -565.43 -31.99 229.64 683.73 -5.57 0.01 839 0.25 0.42 0.41 0.25 1.00
68000 -568.29 -38.68 541.78 1557.41 -5.52 0.01 847 0.25 0.42 0.41 0.25 1.00
70000 -580.56 -31.82 210.86 783.44 -5.48 0.01 860 0.25 0.43 0.41 0.25 1.00
72000 -565.98 -28.59 137.80 468.08 -5.55 0.01 836 0.25 0.43 0.41 0.25 1.00
74000 -567.81 -33.70 297.87 781.20 -5.60 0.01 879 0.25 0.43 0.41 0.25 1.00
76000 -575.76 -33.98 285.42 1019.56 -5.66 0.01 854 0.25 0.43 0.41 0.25 1.00
78000 -573.31 -32.87 257.81 767.95 -5.54 0.01 838 0.25 0.43 0.41 0.25 1.00
80000 -564.28 -35.08 351.86 964.50 -5.60 0.01 849 0.25 0.42 0.41 0.25 1.00
82000 -579.53 -28.43 138.87 429.24 -5.74 0.01 888 0.25 0.42 0.41 0.25 1.00
84000 -565.66 -32.66 240.23 838.26 -5.48 0.01 873 0.25 0.42 0.41 0.25 1.00
86000 -573.71 -28.59 136.79 476.51 -5.57 0.01 857 0.25 0.43 0.41 0.25 1.00
88000 -576.16 -31.93 225.35 698.42 -5.57 0.01 842 0.25 0.43 0.41 0.25 1.00
90000 -574.92 -31.29 206.03 632.96 -5.40 0.01 851 0.25 0.43 0.41 0.25 1.00
92000 -571.82 -28.29 139.53 390.61 -5.54 0.01 874 0.25 0.42 0.41 0.25 1.00
94000 -576.39 -31.20 201.83 654.59 -5.63 0.01 842 0.25 0.43 0.41 0.25 1.00
96000 -575.75 -30.10 165.41 626.99 -5.50 0.01 896 0.25 0.43 0.41 0.25 1.00
98000 -586.96 -28.71 145.72 437.10 -5.68 0.01 853 0.25 0.43 0.41 0.24 1.00
100000 -572.65 -33.31 266.43 877.81 -5.56 0.01 876 0.25 0.43 0.41 0.25 1.00
chain.linear <- mymcmc.linear$load()
chain.linear <- set.burnin(chain.linear, 0.3)
plot(chain.linear)
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
lapply(seq(1, length(chain.linear$gen), 1), function(i) lines(x, chain.linear$beta_intercept[i]+chain.linear$beta_slope[i]*x, col=makeTransparent(rgb(1, 0, 0), alpha=10)))
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
[[830]]
NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
[[904]]
NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
[[938]]
NULL
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NULL
[[940]]
NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
[[961]]
NULL
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NULL
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NULL
[[964]]
NULL
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NULL
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NULL
[[967]]
NULL
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NULL
[[969]]
NULL
[[970]]
NULL
[[971]]
NULL
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NULL
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NULL
[[974]]
NULL
[[975]]
NULL
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NULL
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NULL
[[978]]
NULL
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NULL
[[980]]
NULL
[[981]]
NULL
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NULL
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NULL
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NULL
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NULL
[[987]]
NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
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NULL
[[997]]
NULL
[[998]]
NULL
[[999]]
NULL
[[1000]]
NULL
[[1001]]
NULL
plot(chain.step$lnL, type="n", ylim=c(-700, -500))
lines(1:length(chain.sigmoid$lnL), chain.sigmoid$lnL, col="green")
lines(1:length(chain.linear$lnL), chain.linear$lnL, col="blue")
lines(1:length(chain.step$lnL), chain.step$lnL, col="red")
saveRDS(chain.sigmoid, "../output/OUwie/chain.sigmoid")
saveRDS(chain.linear, "../output/OUwie/chain.linear")
saveRDS(chain.step, "../output/OUwie/chain.step")
Let’s do a likelihood based analysis.
library(optimx)
i <- 10
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
lik.step <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
k = k,
ntheta=ntheta,
theta = ifelse(midbins>pp[5], pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
lik.linear <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_intercept = pp[3],
beta_slope = pp[4],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]*midbins,
sb = sb,
loc=loc,
t2=t2)
model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1), upper=c(1000, 1000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200),
upper=c(1000, 1000, 0, 0, 200))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1),
upper=c(1000, 1000, 0, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics
plot(midbins, startpar.linear$theta, ylim=c(-10, 0), type="n")
x <- -350:200
points(td[['tmin.025']], td[['lnVs']], pch=21, bg="gray50")
Vy.linear <- sqrt(opt.linear$p2/(2*opt.linear$p1))
lines(x, opt.linear$p3+opt.linear$p4*x, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) + 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
lines(x, (opt.linear$p3+opt.linear$p4*x) - 2*Vy.linear, col=makeTransparent(rgb(0, 0, 1), alpha=255), lwd=2, lty=2)
Vy.sigmoid <- opt.sigmoid$p2/(2*opt.sigmoid$p1)
lines(x, opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5))), col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) + 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
lines(x, (opt.sigmoid$p3+opt.sigmoid$p4/(1 + exp(opt.sigmoid$p6*(x-opt.sigmoid$p5)))) - 2*Vy.sigmoid, col=makeTransparent(rgb(0, 1, 0), alpha=255), lwd=2, lty=2)
Vy.step <- opt.step$p2/(2*opt.step$p1)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3), col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) + 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
lines(x, ifelse(x > opt.step$p5, opt.step$p4, opt.step$p3) - 2*Vy.step, col=makeTransparent(rgb(1, 0, 0), alpha=255), lwd=2, lty=2)
## Script it over all trees.
optimizeModelsOverSimmap <- function(i){
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.sigmoid0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
beta_slope = pp[5],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.tanhLin <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
pp.sigmoid0 <- pp.sigmoid[-5]
lik.step <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
k = k,
ntheta=ntheta,
theta = ifelse(midbins>pp[5], pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.step0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
k = k,
ntheta=ntheta,
theta = ifelse(midbins>0, pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]
lik.linear <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_intercept = pp[3],
beta_slope = pp[4],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]*midbins,
sb = sb,
loc=loc,
t2=t2)
model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1),
upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200),
upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1),
upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12),
upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1),
upper=c(2000, 2000, 0, 1))
opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics
return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}
opt.allmodels <- lapply(1:100, optimizeModelsOverSimmap)
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
Paired t-test
data: all.aics$aic.sigmoid and all.aics$aic.linear
t = -3.9995, df = 99, p-value = 0.0001225
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.6718993 -0.5630916
sample estimates:
mean of the differences
-1.117495
tmp <- reshape2::melt(all.aics)
No id variables; using all as measure variables
cat("Akaike weights\n")
Akaike weights
aicws <- aicw(tmp$value)$w
round(tapply(aicws, tmp$variable, sum),2)
aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.52 0.00 0.00 0.42 0.00 0.05
dAICs <- apply(all.aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", ylim=c(0,8))
cat("Mean dAIC values\n")
Mean dAIC values
apply(t(dAICs), 2, mean)
aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
1.4509317 41.1330105 2.5684272 0.2571837 39.1330105 3.3319121
Let’s filter by phenology and analyze each independently. This is not ideal, since transitions occur between deciduous and evergreen species, and are not accounted for when we filter this way. However, if phylogenetic half-lives are small, this is probably of negligible importance.
## Set up the bins:
ncat <- 10
pred <- "tmin.01"
trait <- "lnVs"
bins <- seq(min(td[[pred]])-0.1, max(td[[pred]])+0.1, length.out=ncat)
midbins <- (bins[2:length(bins)] + bins[1:(length(bins)-1)])/2
td <- mutate(td, binpred = as.numeric(cut(td[[pred]], breaks=bins, include.lowest=TRUE)))
hist(td[['binpred']])
bins
[1] -395.10000 -329.11667 -263.13333 -197.15000 -131.16667 -65.18333 0.80000 66.78333 132.76667 198.75000
td$phy$edge.length <- td$phy$edge.length/max(branching.times(td$phy))
tree <- td$phy
dat <- td$dat
tdEV <- filter(td, phenology == "EV")
tdD <- filter(td, phenology == "D")
tdEV$phy <- reorder(tdEV$phy, "postorder")
tdD$phy <- reorder(tdD$phy, "postorder")
#tdEV$phy$edge.length <- tdEV$phy$edge.length/max(branching.times(tdEV$phy))*100
## Use diversitree to fit a meristic model:
mknEV.lik <- make.mkn.meristic(tdEV$phy, tdEV[['binpred']], k = ncat)
mknD.lik <- make.mkn.meristic(tdD$phy, tdD[['binpred']], k = ncat)
mknEV.lik <- constrain(mknEV.lik, q.up~q.down)
mknD.lik <- constrain(mknD.lik, q.up~q.down)
p <- c(0.1)
EVfit.mkn <- find.mle(mknEV.lik, p)
Dfit.mkn <- find.mle(mknD.lik, p)
Q.EV <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
Q.EV[i, i+1] <- EVfit.mkn$par[1]
Q.EV[i+1, i] <- EVfit.mkn$par[1]
}
Q.D <- matrix(0, ncol=ncat-1, nrow=ncat-1)
for(i in 1:(ncat-2)){
Q.D[i, i+1] <- Dfit.mkn$par[1]
Q.D[i+1, i] <- Dfit.mkn$par[1]
}
diag(Q.EV) <- apply(Q.EV, 1, sum)*-1
diag(Q.D) <- apply(Q.D, 1, sum)*-1
rownames(Q.EV) <- colnames(Q.EV) <- 1:ncol(Q.EV)
rownames(Q.D) <- colnames(Q.D) <- 1:ncol(Q.D)
simmap2bayou <- function(stree){
shifts <- sapply(stree$maps, function(x) names(x)[-1])
sb <- unlist(sapply(1:length(shifts), function(x) rep(x, length(shifts[[x]]))))
t2 <- as.numeric(unlist(sapply(stree$maps, function(x) names(x)[-1])))
loc <- unlist(sapply(stree$maps, function(x) cumsum(x)[-1]))
return(list(shifts=shifts, sb=sb, t2=t2, loc=loc))
}
#fdfit <- fitDiscrete(td$phy, td[['binpred']], model="meristic")
#Qg <- geiger:::.Qmatrix.from.gfit(fdfit)
#stree <- reorderSimmap(make.simmap(td$phy, td[['binpred']], Q=Q), "postorder")
strees.EV <- make.simmap(tdEV$phy, tdEV[['binpred']], Q=Q.EV, nsim=100)
make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8 9
1 -4.891797 4.891797 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000 0.000000 0.000000
4 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000 0.000000
5 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000 0.000000
6 0.000000 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000 0.000000
7 0.000000 0.000000 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797 0.000000
8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.891797 -9.783594 4.891797
9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 4.891797 -4.891797
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8 9
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
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Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Some probabilities (slightly?) < 0. Setting p < 0 to zero.Done.
strees.D <- make.simmap(tdD$phy, tdD[['binpred']], Q=Q.D, nsim=100)
make.simmap is sampling character histories conditioned on the transition matrix
Q =
1 2 3 4 5 6 7 8 9
1 -12.19336 12.19336 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
2 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
3 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000 0.00000 0.00000
4 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000 0.00000
5 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000 0.00000
6 0.00000 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000 0.00000
7 0.00000 0.00000 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336 0.00000
8 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 12.19336 -24.38672 12.19336
9 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 12.19336 -12.19336
(specified by the user);
and (mean) root node prior probabilities
pi =
1 2 3 4 5 6 7 8 9
0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111 0.1111111
Done.
for(i in 1:length(strees.EV)) strees.EV[[i]] <- reorderSimmap(strees.EV[[i]], "postorder")
for(i in 1:length(strees.D)) strees.D[[i]] <- reorderSimmap(strees.D[[i]], "postorder")
EVtrees.bayou <- lapply(strees.EV, simmap2bayou)
Dtrees.bayou <- lapply(strees.D, simmap2bayou)
saveRDS(EVtrees.bayou, file="../output/OUwie/EVtrees.rds")
saveRDS(Dtrees.bayou, file="../output/OUwie/Dtrees.rds")
EVtrees.bayou <- readRDS(file="../output/OUwie/EVtrees.rds")
Dtrees.bayou <- readRDS(file="../output/OUwie/Dtrees.rds")
## Starting parameters for each function type:
## Step function
step.starts <- c(0, -8, -4)
startpar.stepEVD <- list(
EV = list(alpha=1, sig2=1,
beta_left=step.starts[2],
beta_right=step.starts[3],
beta_center=step.starts[1],
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
D = list(alpha=1, sig2=1,
beta_left=step.starts[2],
beta_right=step.starts[3],
beta_center=step.starts[1],
k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta=ifelse(midbins > step.starts[1], step.starts[3], step.starts[2]),
sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.stepEVD$EV$theta)
sigmoid.starts <- list(beta_left = -2, beta_right = -6, beta_center = 0, beta_slope = 0.02)
startpar.sigmoidEVD <- list(
EV = list(alpha=1, sig2=0.5,
beta_left=sigmoid.starts$beta_left,
beta_right=sigmoid.starts$beta_right,
beta_center=sigmoid.starts$beta_center,
beta_slope = sigmoid.starts$beta_slope,
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
D = list(alpha=1, sig2=0.5,
beta_left=sigmoid.starts$beta_left,
beta_right=sigmoid.starts$beta_right,
beta_center=sigmoid.starts$beta_center,
beta_slope = sigmoid.starts$beta_slope,
k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= sigmoid.starts$beta_left + sigmoid.starts$beta_right/(1 + exp(sigmoid.starts$beta_slope * (midbins - sigmoid.starts$beta_center))),
sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.sigmoidEVD$EV$theta)
linear.starts <- list(beta_intercept = -5, beta_slope = 0.01)
startpar.linearEVD <- list(
EV = list(alpha=1, sig2=0.5,
beta_intercept=linear.starts$beta_intercept,
beta_slope=linear.starts$beta_slope,
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2),
D = list(alpha=1, sig2=0.5,
beta_intercept=linear.starts$beta_intercept,
beta_slope=linear.starts$beta_slope,
k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1,
theta= linear.starts$beta_intercept + linear.starts$beta_slope*midbins,
sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
)
plot(midbins, startpar.linearEVD$EV$theta)
## Set priors
EV.fixed <- list(k=length(EVtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=EVtrees.bayou[[1]]$sb, loc=EVtrees.bayou[[1]]$loc, t2=EVtrees.bayou[[1]]$t2)
D.fixed <- list(k=length(Dtrees.bayou[[1]]$sb), ntheta=ncat-1, sb=Dtrees.bayou[[1]]$sb, loc=Dtrees.bayou[[1]]$loc, t2=Dtrees.bayou[[1]]$t2)
prior.stepEVD <- list(
EV = make.prior(tdEV$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=EV.fixed),
D = make.prior(tdD$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=D.fixed)
)
prior.sigmoidEVD <- list(
EV = make.prior(tdEV$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dbeta_slope="dlnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dbeta_slope=list(meanlog=-3, sdlog=1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=EV.fixed),
D = make.prior(tdD$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_left="dunif",
dbeta_right="dunif",
dbeta_center="dunif",
dbeta_slope="dlnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_left=list(min=-12, max=0),
dbeta_right=list(min=-12, max=0),
dbeta_center=list(min=-100, max=100),
dbeta_slope=list(meanlog=-3, sdlog=1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=D.fixed)
)
prior.linearEVD <- list(
EV = make.prior(tdEV$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_intercept="dnorm",
dbeta_slope="dnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_intercept=list(mean=-6, sd=1.5),
dbeta_slope=list(mean=0, sd=0.1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=EV.fixed),
D = make.prior(tdD$phy, plot.prior = FALSE,
dists=list(dalpha="dlnorm", dsig2="dhalfcauchy",
dbeta_intercept="dnorm",
dbeta_slope="dnorm",
dsb="fixed", dk="fixed",
dtheta="dnull", dloc="fixed"),
param=list(dalpha=list(meanlog=1, sdlog=1), dsig2=list(scale=0.1),
dbeta_intercept=list(mean=-6, sd=1.5),
dbeta_slope=list(mean=0, sd=0.1),
dk="fixed",
dsb="fixed",
dtheta=list(), dloc="fixed"),
fixed=D.fixed)
)
mymcmcEV.step <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$EV, startpar=startpar.stepEVD$EV, new.dir=TRUE, outname="stepThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.step <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.stepTheta, prior=prior.stepEVD$D, startpar=startpar.stepEVD$D, new.dir=TRUE, outname="stepThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcEV.sigmoid <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$EV, startpar=startpar.sigmoidEVD$EV, new.dir=TRUE, outname="sigmoidThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.sigmoid <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.sigmoidTheta, prior=prior.sigmoidEVD$D, startpar=startpar.sigmoidEVD$D, new.dir=TRUE, outname="sigmoidThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcEV.linear <- bayou.makeMCMC(tdEV$phy, tdEV[[trait]], pred=tdEV['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$EV, startpar=startpar.linearEVD$EV, new.dir=TRUE, outname="linearThetaEV_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
mymcmcD.linear <- bayou.makeMCMC(tdD$phy, tdD[[trait]], pred=tdD['binpred'], SE=0.02, model=model.linearTheta, prior=prior.linearEVD$D, startpar=startpar.linearEVD$D, new.dir=TRUE, outname="linearThetaD_r1", plot.freq=NULL, ticker.freq=1000, samp = 100)
strees.bayou <- EVtrees.bayou
mymcmcEV.step$run(100000)
gen lnL prior alpha sig2 beta_le beta_ri beta_ce k .alpha .beta_c .beta_l .beta_r .sig2 .theta
1000 -389.49 -37.84 123.25 367.89 -7.03 -4.87 -25.93 347 0.40 0.89 0.83 0.84 0.36 1.00
2000 -391.58 -43.97 278.06 939.88 -6.87 -5.03 -8.55 339 0.37 0.93 0.83 0.82 0.31 1.00
3000 -392.95 -39.24 148.78 464.69 -6.78 -4.85 -10.20 324 0.35 0.91 0.82 0.82 0.31 1.00
4000 -383.60 -40.84 195.60 511.91 -6.80 -4.58 -11.58 370 0.34 0.91 0.83 0.82 0.32 1.00
5000 -403.00 -40.82 174.01 689.39 -6.66 -4.95 22.47 351 0.33 0.91 0.82 0.83 0.33 1.00
6000 -384.36 -39.70 157.54 507.07 -6.81 -4.59 -6.65 370 0.33 0.92 0.84 0.84 0.33 1.00
7000 -389.76 -43.39 262.23 829.82 -6.70 -4.88 13.86 325 0.33 0.90 0.84 0.83 0.33 1.00
8000 -395.28 -40.45 172.14 588.95 -6.83 -4.85 -19.79 360 0.32 0.91 0.84 0.83 0.32 1.00
9000 -402.95 -45.02 314.84 1115.19 -6.76 -5.07 -9.86 364 0.32 0.91 0.83 0.83 0.32 1.00
10000 -386.46 -43.48 258.10 906.12 -6.78 -4.88 14.43 365 0.32 0.91 0.83 0.83 0.31 1.00
11000 -393.09 -46.57 375.87 1446.57 -6.80 -4.94 24.70 348 0.32 0.91 0.83 0.83 0.31 1.00
12000 -395.50 -40.75 175.73 649.71 -6.99 -5.00 22.85 331 0.32 0.91 0.83 0.83 0.31 1.00
13000 -389.45 -41.84 201.86 779.10 -6.74 -4.76 29.74 326 0.32 0.91 0.83 0.82 0.31 1.00
14000 -390.01 -42.50 231.29 750.37 -6.69 -4.81 19.73 348 0.32 0.91 0.83 0.82 0.31 1.00
15000 -400.90 -42.79 244.14 748.07 -6.66 -4.85 32.51 358 0.32 0.91 0.84 0.82 0.30 1.00
16000 -385.66 -42.80 243.25 760.22 -6.70 -4.75 27.84 332 0.32 0.91 0.84 0.82 0.30 1.00
17000 -400.21 -39.10 139.64 508.42 -7.04 -4.81 15.16 351 0.31 0.91 0.84 0.82 0.30 1.00
18000 -393.84 -42.58 224.19 848.50 -6.49 -4.66 16.79 350 0.31 0.91 0.84 0.82 0.30 1.00
19000 -395.55 -44.98 313.61 1103.84 -6.80 -4.89 15.81 350 0.31 0.91 0.84 0.82 0.30 1.00
20000 -398.85 -39.82 153.06 580.66 -6.89 -4.79 -21.83 342 0.31 0.91 0.84 0.82 0.30 1.00
21000 -396.40 -42.09 209.37 800.72 -6.89 -4.79 9.78 357 0.31 0.92 0.84 0.82 0.29 1.00
22000 -389.66 -44.13 281.43 984.07 -6.78 -4.78 17.18 325 0.31 0.92 0.84 0.83 0.30 1.00
23000 -413.53 -38.11 128.30 380.73 -6.69 -4.83 17.46 352 0.31 0.92 0.84 0.83 0.30 1.00
24000 -398.98 -40.60 176.04 597.82 -6.67 -4.77 -19.42 358 0.31 0.92 0.84 0.83 0.30 1.00
25000 -406.83 -40.81 178.14 644.24 -6.85 -4.81 17.23 333 0.31 0.92 0.84 0.83 0.30 1.00
26000 -391.49 -42.63 232.31 793.36 -6.80 -4.60 10.93 348 0.31 0.92 0.84 0.83 0.30 1.00
27000 -406.05 -39.63 148.27 571.46 -6.69 -4.93 -6.03 364 0.31 0.92 0.84 0.83 0.30 1.00
28000 -392.42 -41.14 198.64 571.78 -6.90 -5.13 -20.97 353 0.31 0.92 0.84 0.83 0.30 1.00
29000 -400.75 -40.46 169.53 616.89 -6.85 -4.79 4.53 333 0.31 0.92 0.84 0.83 0.30 1.00
30000 -393.68 -40.38 176.09 535.25 -6.79 -4.94 11.73 313 0.31 0.92 0.84 0.83 0.30 1.00
31000 -392.74 -41.88 222.37 612.96 -6.79 -4.83 21.27 341 0.31 0.92 0.84 0.83 0.30 1.00
32000 -385.88 -41.96 223.52 629.56 -6.77 -4.78 -18.64 361 0.31 0.92 0.84 0.83 0.30 1.00
33000 -401.70 -39.50 140.93 606.62 -6.89 -4.68 -18.78 339 0.31 0.92 0.84 0.83 0.30 1.00
34000 -387.65 -41.48 207.75 601.08 -6.90 -4.68 -21.06 365 0.31 0.92 0.84 0.83 0.30 1.00
35000 -393.17 -41.79 216.03 634.00 -6.78 -4.77 26.11 332 0.31 0.92 0.84 0.83 0.30 1.00
36000 -388.83 -41.93 206.06 769.54 -6.80 -4.83 -1.35 330 0.31 0.92 0.84 0.83 0.30 1.00
37000 -397.23 -42.16 215.54 765.58 -6.84 -4.44 -14.63 342 0.31 0.92 0.84 0.83 0.30 1.00
38000 -408.26 -38.70 133.86 462.40 -6.70 -4.92 23.39 333 0.31 0.92 0.84 0.83 0.30 1.00
39000 -386.80 -40.06 166.35 529.57 -6.79 -4.59 -30.30 327 0.31 0.92 0.84 0.83 0.30 1.00
40000 -401.33 -44.19 294.21 895.00 -6.52 -4.99 31.11 358 0.31 0.92 0.84 0.83 0.30 1.00
41000 -393.62 -42.32 219.07 796.21 -6.68 -4.80 -3.42 348 0.31 0.92 0.84 0.83 0.30 1.00
42000 -394.74 -40.80 182.10 605.73 -6.79 -4.73 -15.75 360 0.31 0.92 0.84 0.83 0.30 1.00
43000 -384.84 -43.36 266.37 780.68 -6.79 -4.77 -1.18 332 0.31 0.92 0.84 0.83 0.30 1.00
44000 -378.56 -45.21 333.69 1035.86 -6.72 -4.79 3.28 370 0.31 0.92 0.84 0.83 0.30 1.00
45000 -396.24 -46.72 400.07 1290.01 -6.95 -4.68 -7.82 336 0.31 0.92 0.84 0.83 0.30 1.00
46000 -393.99 -40.11 165.30 550.41 -6.71 -4.96 4.05 375 0.31 0.92 0.84 0.83 0.30 1.00
47000 -388.50 -42.13 216.93 740.60 -6.65 -4.93 2.32 349 0.31 0.92 0.84 0.83 0.30 1.00
48000 -397.35 -44.46 305.00 925.52 -6.75 -5.04 -24.27 333 0.31 0.92 0.84 0.83 0.30 1.00
49000 -406.00 -42.87 232.42 892.00 -6.89 -4.94 -3.16 378 0.31 0.92 0.84 0.83 0.29 1.00
50000 -395.63 -49.18 542.50 1732.73 -6.62 -4.70 26.58 326 0.31 0.92 0.84 0.83 0.29 1.00
51000 -395.34 -41.23 184.91 722.11 -6.74 -4.87 4.33 346 0.31 0.92 0.84 0.83 0.29 1.00
52000 -393.86 -43.92 275.44 942.99 -6.75 -5.01 -2.60 357 0.31 0.92 0.84 0.83 0.29 1.00
53000 -402.29 -38.98 131.80 552.64 -6.73 -4.99 -16.72 333 0.31 0.92 0.84 0.83 0.29 1.00
54000 -390.22 -39.02 145.83 437.62 -6.76 -4.85 -4.37 361 0.31 0.92 0.84 0.83 0.29 1.00
55000 -393.85 -41.81 207.54 711.17 -6.90 -4.83 31.00 332 0.31 0.93 0.84 0.83 0.29 1.00
56000 -389.99 -46.97 410.00 1361.11 -6.84 -4.76 -24.89 375 0.31 0.93 0.84 0.83 0.29 1.00
57000 -390.06 -44.38 295.97 966.77 -6.89 -4.68 -5.27 344 0.31 0.93 0.84 0.83 0.29 1.00
58000 -390.89 -43.05 244.40 848.59 -6.77 -4.72 -12.91 341 0.31 0.93 0.84 0.83 0.29 1.00
59000 -395.00 -48.49 478.47 1816.41 -6.84 -4.73 17.38 360 0.31 0.92 0.84 0.83 0.29 1.00
60000 -397.08 -41.51 198.91 687.09 -7.10 -4.75 19.48 346 0.31 0.93 0.84 0.83 0.29 1.00
61000 -395.70 -42.86 252.39 706.34 -6.79 -4.98 24.44 350 0.31 0.93 0.84 0.83 0.30 1.00
62000 -386.34 -44.78 309.56 1040.87 -6.72 -4.91 22.96 330 0.31 0.93 0.84 0.83 0.30 1.00
63000 -385.14 -43.25 256.21 825.96 -6.70 -4.85 20.95 334 0.31 0.93 0.84 0.83 0.30 1.00
64000 -402.62 -41.89 221.60 620.55 -6.76 -4.74 28.11 352 0.31 0.93 0.84 0.83 0.30 1.00
65000 -396.89 -44.53 304.26 961.51 -6.54 -4.79 20.47 320 0.31 0.93 0.84 0.83 0.30 1.00
66000 -378.04 -45.15 334.87 995.20 -6.75 -4.77 -8.92 370 0.31 0.93 0.84 0.83 0.29 1.00
67000 -393.12 -41.94 209.97 737.75 -6.56 -4.68 11.97 375 0.31 0.92 0.84 0.83 0.30 1.00
68000 -390.80 -41.78 212.03 661.95 -6.87 -4.85 15.86 324 0.31 0.92 0.84 0.83 0.29 1.00
69000 -397.92 -39.92 165.58 500.23 -6.62 -4.64 15.76 343 0.31 0.92 0.84 0.83 0.29 1.00
70000 -404.76 -45.58 339.91 1185.09 -6.60 -4.69 -23.19 333 0.31 0.92 0.84 0.83 0.29 1.00
71000 -386.45 -41.38 208.54 565.43 -6.86 -4.84 6.24 327 0.31 0.92 0.84 0.83 0.29 1.00
72000 -401.90 -39.05 136.13 528.74 -6.84 -4.68 9.90 342 0.31 0.92 0.84 0.83 0.29 1.00
73000 -414.37 -36.12 89.74 325.58 -6.58 -5.16 22.22 333 0.31 0.92 0.84 0.83 0.29 1.00
74000 -389.69 -44.12 293.98 864.97 -6.76 -4.86 17.09 344 0.31 0.92 0.84 0.83 0.29 1.00
75000 -389.78 -42.20 214.08 795.76 -6.75 -4.88 -17.28 353 0.31 0.92 0.84 0.83 0.29 1.00
76000 -391.23 -41.09 187.42 650.00 -6.84 -4.59 -14.33 353 0.31 0.92 0.84 0.83 0.29 1.00
77000 -391.60 -41.85 199.04 813.63 -6.75 -4.97 -23.95 365 0.31 0.92 0.84 0.83 0.29 1.00
78000 -379.49 -47.37 431.10 1427.52 -6.86 -4.97 -26.45 370 0.31 0.92 0.84 0.83 0.29 1.00
79000 -384.61 -46.11 372.18 1180.43 -6.64 -4.88 31.74 334 0.31 0.92 0.84 0.83 0.29 1.00
80000 -384.44 -47.68 442.42 1544.49 -6.75 -4.77 -8.00 334 0.31 0.92 0.84 0.83 0.29 1.00
81000 -389.82 -40.40 175.85 544.47 -6.85 -4.96 2.74 353 0.31 0.92 0.84 0.83 0.29 1.00
82000 -387.05 -42.59 238.91 720.25 -6.79 -4.60 -21.93 349 0.31 0.92 0.84 0.83 0.29 1.00
83000 -391.86 -44.26 282.39 1040.74 -6.95 -4.88 13.49 360 0.31 0.92 0.84 0.83 0.29 1.00
84000 -390.86 -40.65 177.18 605.51 -6.71 -4.71 -1.73 353 0.31 0.92 0.84 0.83 0.29 1.00
85000 -394.09 -40.74 183.19 580.14 -6.91 -4.80 0.43 332 0.31 0.92 0.84 0.83 0.29 1.00
86000 -393.36 -40.68 178.91 597.94 -6.96 -4.95 -21.63 348 0.31 0.92 0.84 0.83 0.29 1.00
87000 -409.43 -37.88 113.33 457.79 -6.57 -5.13 -23.85 378 0.31 0.92 0.84 0.83 0.29 1.00
88000 -397.91 -41.66 208.74 650.50 -6.67 -5.12 -15.26 357 0.31 0.92 0.84 0.83 0.29 1.00
89000 -403.88 -43.81 265.21 991.54 -6.61 -4.68 -17.81 364 0.31 0.92 0.84 0.83 0.29 1.00
90000 -390.30 -40.00 168.48 496.10 -6.79 -4.74 10.70 371 0.31 0.92 0.84 0.83 0.29 1.00
91000 -393.33 -39.93 156.78 574.93 -6.74 -4.74 1.36 348 0.31 0.92 0.84 0.83 0.29 1.00
92000 -392.35 -48.18 512.55 1254.55 -6.75 -4.96 -5.62 339 0.31 0.92 0.84 0.83 0.29 1.00
93000 -392.23 -42.94 239.62 849.17 -6.77 -5.10 -7.29 342 0.31 0.92 0.84 0.83 0.29 1.00
94000 -403.08 -45.86 339.64 1367.28 -6.48 -4.85 14.54 364 0.31 0.92 0.84 0.83 0.29 1.00
95000 -401.07 -39.77 150.27 591.64 -6.96 -4.85 21.10 339 0.31 0.92 0.84 0.83 0.29 1.00
96000 -391.10 -46.58 384.20 1360.43 -6.71 -4.81 -24.22 332 0.31 0.92 0.84 0.83 0.29 1.00
97000 -399.44 -41.16 188.94 657.80 -6.70 -5.00 -12.29 342 0.31 0.92 0.84 0.83 0.29 1.00
98000 -386.43 -40.75 186.23 557.66 -6.92 -4.74 1.27 346 0.31 0.93 0.84 0.83 0.29 1.00
99000 -404.54 -38.42 131.17 422.74 -6.83 -4.90 -8.60 321 0.31 0.93 0.84 0.83 0.29 1.00
100000 -402.55 -43.67 261.22 965.18 -6.60 -4.84 19.49 364 0.31 0.93 0.84 0.83 0.29 1.00
chainEV.step <- mymcmcEV.step$load()
chainEV.step <- set.burnin(chainEV.step, 0.3)
plot(chainEV.step)
strees.bayou <- Dtrees.bayou
mymcmcD.step$run(100000)
gen lnL prior alpha sig2 beta_le beta_ri beta_ce k .alpha .beta_c .beta_l .beta_r .sig2 .theta
1000 -171.01 -25.69 15.15 43.81 -6.74 -4.35 -96.48 423 0.55 0.98 0.91 0.93 0.37 1.00
2000 -174.17 -24.39 10.40 36.71 -6.07 -4.48 13.69 423 0.53 0.97 0.89 0.94 0.35 1.00
3000 -177.91 -23.60 9.21 28.46 -6.30 -4.49 -77.11 352 0.49 0.97 0.87 0.93 0.36 1.00
4000 -177.86 -24.82 12.69 35.63 -6.05 -5.00 -62.49 372 0.48 0.93 0.87 0.92 0.37 1.00
5000 -165.96 -25.87 17.34 39.70 -6.70 -4.49 -72.05 390 0.48 0.93 0.88 0.92 0.37 1.00
6000 -177.56 -23.84 8.79 33.75 -5.68 -3.78 -1.20 396 0.47 0.93 0.88 0.91 0.38 1.00
7000 -183.75 -23.20 6.77 31.93 -6.13 -3.79 60.75 437 0.47 0.93 0.88 0.92 0.38 1.00
8000 -183.96 -24.31 11.27 32.03 -5.38 -3.66 59.95 390 0.48 0.93 0.88 0.93 0.38 1.00
9000 -178.99 -23.83 9.35 31.28 -5.87 -3.97 91.09 404 0.48 0.93 0.88 0.93 0.38 1.00
10000 -175.59 -25.05 12.64 40.35 -6.05 -4.12 -27.01 372 0.48 0.93 0.88 0.93 0.39 1.00
11000 -178.08 -27.55 21.27 67.81 -5.91 -4.17 -27.87 391 0.48 0.92 0.88 0.93 0.39 1.00
12000 -179.36 -24.02 8.60 37.84 -5.78 -4.94 -56.76 379 0.48 0.93 0.88 0.93 0.39 1.00
13000 -177.17 -25.21 12.54 44.00 -5.67 -4.47 60.52 388 0.48 0.93 0.89 0.93 0.39 1.00
14000 -177.63 -24.62 12.36 33.44 -5.89 -3.76 69.03 409 0.48 0.93 0.89 0.93 0.39 1.00
15000 -175.11 -27.62 21.29 70.33 -6.21 -4.73 -69.72 374 0.48 0.93 0.90 0.93 0.39 1.00
16000 -181.41 -24.02 8.85 36.57 -6.74 -4.62 -67.26 420 0.48 0.93 0.90 0.93 0.39 1.00
17000 -177.04 -24.80 11.82 38.67 -6.22 -4.71 -86.54 409 0.48 0.93 0.89 0.93 0.40 1.00
18000 -177.77 -22.22 5.86 22.40 -6.75 -3.87 -64.37 398 0.48 0.93 0.90 0.93 0.40 1.00
19000 -172.44 -25.47 13.64 45.03 -6.53 -4.55 -38.13 357 0.48 0.93 0.90 0.93 0.40 1.00
20000 -176.64 -24.11 9.72 34.52 -6.46 -4.58 -79.74 372 0.48 0.93 0.90 0.93 0.40 1.00
21000 -175.20 -24.51 11.37 35.00 -6.01 -4.57 -4.09 403 0.48 0.94 0.90 0.93 0.40 1.00
22000 -178.86 -23.34 8.16 28.52 -6.11 -4.12 33.78 386 0.47 0.94 0.90 0.93 0.40 1.00
23000 -176.80 -23.69 8.87 30.92 -6.48 -3.87 15.17 406 0.47 0.94 0.90 0.93 0.40 1.00
24000 -182.82 -23.11 7.92 26.17 -5.61 -3.64 93.69 435 0.47 0.94 0.90 0.93 0.40 1.00
25000 -186.23 -22.92 5.70 32.62 -5.29 -2.88 57.97 392 0.47 0.94 0.90 0.93 0.40 1.00
26000 -179.14 -23.50 7.91 31.74 -5.68 -4.16 42.02 366 0.47 0.94 0.90 0.93 0.40 1.00
27000 -177.35 -23.01 7.57 26.05 -5.67 -4.59 20.69 381 0.47 0.94 0.90 0.93 0.40 1.00
28000 -179.55 -22.86 6.56 27.85 -5.87 -3.95 4.11 401 0.47 0.94 0.90 0.93 0.40 1.00
29000 -182.47 -22.01 5.71 20.58 -5.69 -3.90 95.86 391 0.48 0.94 0.90 0.93 0.40 1.00
30000 -181.10 -23.45 7.05 34.84 -5.85 -4.65 68.03 379 0.48 0.94 0.90 0.93 0.40 1.00
31000 -180.90 -22.87 6.21 29.34 -6.03 -4.48 29.61 374 0.48 0.94 0.90 0.93 0.40 1.00
32000 -184.70 -26.22 16.00 52.82 -5.82 -4.13 -3.89 404 0.48 0.94 0.90 0.93 0.40 1.00
33000 -177.09 -25.42 14.86 39.21 -6.77 -5.19 -98.59 374 0.48 0.93 0.90 0.93 0.40 1.00
34000 -179.83 -22.47 5.60 26.34 -7.11 -4.84 -71.14 394 0.47 0.93 0.90 0.93 0.40 1.00
35000 -178.03 -25.43 14.31 41.53 -6.15 -4.53 -86.83 391 0.47 0.93 0.90 0.93 0.40 1.00
36000 -181.82 -23.85 8.41 35.59 -6.08 -5.25 -44.11 391 0.47 0.93 0.90 0.93 0.40 1.00
37000 -179.72 -27.98 22.47 77.30 -6.17 -4.07 3.13 383 0.47 0.93 0.90 0.93 0.40 1.00
38000 -179.67 -26.44 15.93 59.47 -5.96 -4.50 -30.31 403 0.47 0.93 0.90 0.93 0.40 1.00
39000 -177.94 -23.52 7.91 32.23 -5.93 -4.50 0.86 380 0.47 0.93 0.90 0.93 0.40 1.00
40000 -180.59 -23.09 7.30 28.20 -5.40 -4.64 80.16 403 0.47 0.93 0.90 0.93 0.40 1.00
41000 -183.54 -24.52 11.30 35.50 -5.31 -4.13 51.20 426 0.47 0.93 0.90 0.93 0.40 1.00
42000 -179.63 -24.34 10.41 35.69 -6.52 -4.58 -56.26 370 0.47 0.93 0.90 0.93 0.40 1.00
43000 -177.41 -25.15 14.15 36.50 -6.11 -4.26 -22.26 404 0.47 0.93 0.90 0.93 0.40 1.00
44000 -177.70 -24.00 9.03 35.58 -6.29 -4.53 -59.78 381 0.47 0.93 0.90 0.93 0.40 1.00
45000 -179.94 -24.58 11.74 34.86 -5.80 -4.80 -7.82 394 0.47 0.93 0.90 0.93 0.40 1.00
46000 -183.01 -24.93 11.66 41.83 -5.69 -4.16 7.71 407 0.47 0.93 0.90 0.93 0.41 1.00
47000 -173.66 -24.58 12.76 31.52 -5.79 -4.26 -1.65 398 0.47 0.93 0.90 0.93 0.41 1.00
48000 -175.61 -28.05 23.34 75.34 -6.39 -4.32 -28.94 389 0.47 0.93 0.90 0.93 0.41 1.00
49000 -175.88 -23.43 7.46 32.53 -6.16 -4.64 -88.48 357 0.47 0.93 0.90 0.93 0.41 1.00
50000 -179.32 -24.21 10.05 34.83 -5.98 -4.56 -29.40 426 0.47 0.93 0.90 0.93 0.41 1.00
51000 -181.49 -24.65 10.91 39.46 -5.84 -4.18 12.50 383 0.47 0.93 0.90 0.93 0.41 1.00
52000 -172.94 -25.75 14.39 48.27 -6.53 -4.77 -90.18 389 0.47 0.93 0.90 0.93 0.41 1.00
53000 -183.86 -22.98 7.08 27.42 -6.61 -4.88 -40.08 391 0.47 0.93 0.90 0.93 0.41 1.00
54000 -180.74 -23.91 10.59 28.26 -6.28 -4.10 -4.94 384 0.47 0.93 0.90 0.93 0.41 1.00
55000 -183.33 -24.70 11.92 36.34 -5.73 -3.97 93.91 407 0.47 0.93 0.90 0.93 0.41 1.00
56000 -178.05 -24.48 10.18 39.29 -5.50 -4.48 8.34 369 0.47 0.93 0.90 0.93 0.41 1.00
57000 -178.65 -23.70 8.56 32.29 -6.03 -3.91 70.14 403 0.47 0.93 0.90 0.93 0.41 1.00
58000 -173.95 -23.37 8.21 28.62 -5.60 -3.37 52.31 381 0.47 0.93 0.90 0.93 0.41 1.00
59000 -181.67 -22.09 5.39 22.50 -6.07 -3.73 83.62 406 0.47 0.93 0.90 0.93 0.41 1.00
60000 -172.09 -26.08 16.23 48.36 -6.65 -4.41 -76.53 369 0.47 0.93 0.90 0.93 0.41 1.00
61000 -174.23 -23.36 8.82 26.48 -6.78 -4.48 -44.25 366 0.47 0.93 0.90 0.93 0.41 1.00
62000 -180.06 -26.81 19.72 52.55 -5.88 -4.17 -0.57 357 0.47 0.93 0.90 0.93 0.41 1.00
63000 -180.14 -24.18 9.61 36.22 -5.59 -4.29 62.08 390 0.47 0.93 0.90 0.93 0.41 1.00
64000 -184.30 -26.12 18.26 41.64 -5.97 -3.68 4.83 384 0.47 0.93 0.90 0.93 0.41 1.00
65000 -180.73 -24.13 9.14 37.33 -5.46 -4.67 -27.27 408 0.47 0.93 0.89 0.93 0.41 1.00
66000 -183.62 -23.22 7.80 28.03 -6.47 -3.18 34.87 366 0.47 0.93 0.89 0.93 0.41 1.00
67000 -176.80 -24.55 12.34 32.30 -6.10 -4.16 0.68 408 0.47 0.93 0.89 0.93 0.41 1.00
68000 -177.00 -24.64 12.48 33.38 -5.72 -4.56 -2.47 357 0.47 0.93 0.89 0.93 0.41 1.00
69000 -182.43 -22.75 5.56 30.55 -5.94 -4.79 -84.40 403 0.47 0.93 0.89 0.93 0.41 1.00
70000 -173.31 -28.06 23.71 73.79 -6.42 -4.62 -71.27 404 0.47 0.93 0.89 0.93 0.41 1.00
71000 -181.27 -22.27 5.56 24.05 -6.32 -4.83 12.58 380 0.47 0.93 0.89 0.93 0.41 1.00
72000 -177.52 -25.15 12.34 43.57 -6.10 -4.99 -77.91 387 0.47 0.94 0.89 0.93 0.41 1.00
73000 -178.40 -23.44 9.10 26.57 -6.30 -4.59 -56.17 352 0.47 0.94 0.90 0.93 0.41 1.00
74000 -178.22 -24.88 11.10 43.51 -6.13 -5.05 -70.99 412 0.47 0.94 0.90 0.93 0.41 1.00
75000 -174.84 -25.68 14.21 47.29 -6.26 -4.46 -56.01 396 0.47 0.94 0.90 0.93 0.41 1.00
76000 -181.69 -25.26 10.83 53.97 -5.94 -4.56 12.89 352 0.47 0.94 0.90 0.93 0.41 1.00
77000 -178.65 -23.94 9.32 33.32 -5.89 -5.05 -56.78 392 0.47 0.94 0.90 0.93 0.41 1.00
78000 -180.42 -26.41 15.44 61.15 -6.16 -5.06 -29.35 366 0.47 0.94 0.90 0.93 0.41 1.00
79000 -178.44 -23.30 8.95 25.22 -6.27 -5.02 -88.35 379 0.47 0.94 0.90 0.93 0.41 1.00
80000 -176.94 -25.68 13.28 51.90 -6.10 -4.61 -65.19 383 0.47 0.94 0.90 0.93 0.41 1.00
81000 -177.49 -25.70 14.69 45.76 -5.95 -4.83 -60.95 393 0.47 0.94 0.90 0.93 0.41 1.00
82000 -179.53 -22.95 6.60 28.97 -6.80 -4.89 -87.57 404 0.47 0.94 0.90 0.93 0.41 1.00
83000 -178.96 -25.09 12.90 40.10 -6.44 -4.66 -14.40 412 0.47 0.94 0.90 0.93 0.41 1.00
84000 -172.35 -26.79 19.27 53.92 -6.35 -4.70 -81.40 394 0.47 0.94 0.90 0.93 0.41 1.00
85000 -182.17 -22.27 5.57 23.97 -6.72 -4.16 -54.86 392 0.47 0.94 0.90 0.93 0.41 1.00
86000 -179.01 -24.97 11.98 41.39 -5.96 -5.15 -34.18 399 0.47 0.94 0.90 0.93 0.41 1.00
87000 -175.74 -23.80 9.87 29.02 -6.54 -4.77 -94.20 391 0.47 0.94 0.90 0.93 0.41 1.00
88000 -179.49 -25.86 14.54 50.29 -5.92 -5.12 -71.84 404 0.47 0.94 0.90 0.93 0.41 1.00
89000 -174.97 -25.66 13.99 47.98 -6.42 -4.52 -44.37 379 0.47 0.94 0.90 0.93 0.41 1.00
90000 -179.57 -24.83 11.88 38.93 -6.20 -4.41 25.35 381 0.47 0.94 0.90 0.93 0.41 1.00
91000 -179.47 -24.29 9.44 38.93 -5.90 -4.65 89.30 415 0.47 0.94 0.90 0.93 0.41 1.00
92000 -180.13 -23.30 6.81 33.37 -6.01 -4.03 -0.48 390 0.47 0.94 0.90 0.93 0.41 1.00
93000 -179.58 -27.28 20.83 61.13 -5.94 -4.76 -46.65 357 0.47 0.94 0.90 0.93 0.41 1.00
94000 -179.80 -24.16 10.48 32.48 -6.49 -4.15 -81.58 352 0.47 0.94 0.90 0.93 0.41 1.00
95000 -177.91 -23.94 8.95 34.82 -6.43 -5.20 -69.95 379 0.47 0.94 0.90 0.93 0.41 1.00
96000 -174.04 -24.18 11.24 30.16 -6.37 -4.81 -79.04 408 0.47 0.94 0.90 0.93 0.41 1.00
97000 -178.15 -23.79 8.78 32.96 -6.33 -4.79 -5.54 366 0.47 0.94 0.90 0.93 0.41 1.00
98000 -181.99 -22.69 5.95 27.90 -5.34 -4.72 -8.72 404 0.47 0.94 0.90 0.93 0.41 1.00
99000 -175.61 -23.08 7.02 29.07 -6.06 -3.91 11.24 407 0.47 0.94 0.90 0.93 0.41 1.00
100000 -183.60 -21.18 3.90 18.25 -5.94 -3.13 -27.03 411 0.47 0.94 0.90 0.93 0.41 1.00
chainD.step <- mymcmcD.step$load()
chainD.step <- set.burnin(chainD.step, 0.3)
plot(chainD.step)
strees.bayou <- EVtrees.bayou
mymcmcEV.sigmoid$run(100000)
gen lnL prior alpha sig2 beta_le beta_ri beta_ce beta_sl k .alpha .beta_c .beta_l .beta_r .beta_s .sig2 .theta
1000 -381.94 -44.78 434.51 1320.44 -2.01 -5.52 86.13 0.01 343 0.47 0.83 0.84 0.85 0.73 0.39 1.00
2000 -385.74 -39.95 217.74 756.84 -1.57 -6.82 80.25 0.01 369 0.38 0.77 0.80 0.85 0.59 0.36 1.00
3000 -378.12 -38.91 206.87 533.67 -1.74 -6.66 66.78 0.01 314 0.34 0.75 0.79 0.84 0.54 0.35 1.00
4000 -369.24 -44.48 432.56 1020.03 -1.43 -8.05 18.34 0.01 375 0.32 0.75 0.79 0.84 0.50 0.35 1.00
5000 -384.94 -36.97 149.32 412.83 -0.99 -8.50 63.16 0.01 375 0.32 0.74 0.78 0.84 0.47 0.34 1.00
6000 -366.57 -39.91 229.47 642.41 -1.04 -8.58 39.82 0.01 347 0.30 0.73 0.78 0.84 0.44 0.34 1.00
7000 -383.51 -46.14 467.23 1541.03 -0.19 -8.85 98.80 0.01 336 0.30 0.72 0.77 0.84 0.42 0.33 1.00
8000 -374.70 -42.44 298.88 874.87 -0.21 -9.49 78.04 0.01 364 0.31 0.71 0.76 0.84 0.40 0.32 1.00
9000 -373.87 -40.79 241.31 783.22 -0.26 -10.16 39.10 0.01 347 0.31 0.72 0.76 0.84 0.38 0.32 1.00
10000 -371.61 -44.54 378.26 1145.61 -0.21 -10.12 58.78 0.00 353 0.31 0.72 0.75 0.84 0.37 0.32 1.00
11000 -378.59 -40.44 233.13 660.72 -0.52 -10.77 -1.15 0.01 344 0.31 0.71 0.75 0.85 0.36 0.32 1.00
12000 -380.84 -46.72 490.15 1521.05 -0.67 -10.84 -34.50 0.00 364 0.30 0.71 0.75 0.85 0.36 0.31 1.00
13000 -373.32 -42.69 317.55 742.27 -0.58 -10.44 16.78 0.00 348 0.30 0.71 0.75 0.85 0.35 0.31 1.00
14000 -367.79 -44.31 376.06 1158.79 -0.13 -10.04 68.00 0.01 330 0.31 0.71 0.75 0.85 0.34 0.31 1.00
15000 -370.96 -40.14 231.31 611.28 -0.55 -9.55 48.99 0.01 329 0.31 0.71 0.75 0.86 0.33 0.30 1.00
16000 -375.12 -42.32 294.23 897.33 -1.18 -9.59 -12.04 0.01 338 0.31 0.71 0.75 0.86 0.33 0.30 1.00
17000 -383.17 -36.13 123.94 387.67 -1.43 -9.64 -23.03 0.01 364 0.31 0.70 0.74 0.86 0.33 0.30 1.00
18000 -377.07 -41.40 265.71 808.54 -1.61 -8.90 -14.00 0.01 375 0.31 0.71 0.75 0.86 0.33 0.30 1.00
19000 -374.82 -38.16 178.75 464.33 -1.64 -8.44 2.99 0.01 342 0.31 0.70 0.75 0.86 0.33 0.30 1.00
20000 -373.36 -40.48 243.97 713.56 -1.49 -8.17 17.81 0.01 334 0.31 0.70 0.75 0.86 0.33 0.30 1.00
21000 -370.91 -41.89 304.73 810.19 -3.00 -7.55 -57.26 0.01 347 0.31 0.70 0.75 0.86 0.33 0.30 1.00
22000 -376.60 -42.48 319.86 952.73 -3.07 -7.37 -63.78 0.01 375 0.31 0.70 0.75 0.86 0.33 0.30 1.00
23000 -379.68 -39.51 210.06 656.10 -1.85 -7.75 9.35 0.01 375 0.31 0.70 0.75 0.86 0.33 0.30 1.00
24000 -377.61 -41.62 285.88 826.90 -1.26 -7.21 79.59 0.01 375 0.31 0.69 0.75 0.86 0.33 0.30 1.00
25000 -380.41 -41.28 272.84 717.47 -1.16 -8.01 37.46 0.01 323 0.31 0.69 0.75 0.86 0.33 0.30 1.00
26000 -376.76 -43.34 352.88 1080.64 -0.96 -7.45 92.70 0.01 339 0.31 0.69 0.75 0.85 0.33 0.30 1.00
27000 -376.33 -42.61 322.71 897.90 -1.64 -7.58 23.84 0.01 361 0.31 0.69 0.75 0.85 0.33 0.31 1.00
28000 -386.15 -43.03 342.29 812.92 -2.34 -8.08 -39.18 0.01 321 0.31 0.69 0.75 0.85 0.33 0.31 1.00
29000 -376.79 -41.15 266.32 741.19 -2.32 -7.70 -25.77 0.01 325 0.31 0.69 0.75 0.85 0.33 0.31 1.00
30000 -388.41 -43.88 384.61 985.88 -2.63 -7.15 -35.71 0.01 310 0.30 0.69 0.76 0.85 0.34 0.31 1.00
31000 -376.92 -48.99 685.50 1945.54 -2.63 -7.92 -67.75 0.01 344 0.31 0.68 0.76 0.85 0.34 0.31 1.00
32000 -366.26 -45.32 469.32 1175.71 -2.70 -8.31 -69.93 0.01 368 0.31 0.69 0.76 0.86 0.34 0.31 1.00
33000 -378.60 -38.94 199.95 582.00 -2.94 -8.03 -71.70 0.01 367 0.31 0.68 0.76 0.86 0.34 0.31 1.00
34000 -373.92 -43.79 395.69 980.85 -2.70 -7.54 -42.63 0.01 353 0.31 0.68 0.76 0.86 0.34 0.31 1.00
35000 -363.76 -42.98 337.45 932.52 -2.61 -8.85 -81.21 0.01 332 0.30 0.69 0.76 0.86 0.33 0.31 1.00
36000 -388.09 -39.78 206.63 664.83 -1.82 -9.30 -46.73 0.01 310 0.30 0.69 0.76 0.86 0.33 0.31 1.00
37000 -375.91 -43.81 358.60 1035.42 -1.70 -8.64 -11.89 0.01 360 0.30 0.69 0.76 0.86 0.33 0.31 1.00
38000 -378.57 -40.23 247.19 618.57 -2.54 -7.33 -36.81 0.01 364 0.30 0.69 0.76 0.86 0.33 0.31 1.00
39000 -369.74 -43.67 372.60 995.49 -2.92 -7.12 -52.95 0.01 332 0.30 0.69 0.76 0.86 0.33 0.31 1.00
40000 -374.96 -41.78 287.08 949.12 -3.51 -6.77 -64.98 0.01 339 0.30 0.69 0.76 0.86 0.33 0.31 1.00
41000 -370.15 -40.56 258.74 632.09 -2.63 -7.23 -31.18 0.01 332 0.30 0.69 0.76 0.86 0.34 0.30 1.00
42000 -372.65 -38.93 209.07 560.99 -2.85 -6.53 -10.56 0.01 327 0.30 0.69 0.76 0.86 0.34 0.30 1.00
43000 -383.06 -45.44 448.36 1539.55 -2.82 -6.31 -4.07 0.01 333 0.30 0.69 0.76 0.86 0.34 0.30 1.00
44000 -371.88 -37.40 170.56 455.52 -2.42 -5.71 47.97 0.01 332 0.30 0.69 0.76 0.86 0.35 0.30 1.00
45000 -376.17 -40.87 270.89 694.75 -3.25 -6.38 -40.17 0.01 360 0.30 0.69 0.76 0.86 0.35 0.30 1.00
46000 -373.97 -41.95 307.28 858.50 -3.60 -5.45 -40.75 0.01 343 0.30 0.69 0.75 0.86 0.35 0.30 1.00
47000 -380.72 -39.98 237.09 719.98 -3.87 -4.48 -19.18 0.02 352 0.30 0.69 0.76 0.86 0.36 0.30 1.00
48000 -384.12 -39.43 215.56 702.13 -4.32 -3.38 -7.92 0.02 351 0.30 0.69 0.76 0.87 0.37 0.30 1.00
49000 -379.65 -38.70 203.94 566.72 -4.65 -3.48 -42.85 0.02 320 0.30 0.69 0.76 0.87 0.38 0.30 1.00
50000 -371.17 -40.06 231.70 758.87 -4.53 -3.22 -29.57 0.03 368 0.30 0.69 0.76 0.87 0.39 0.30 1.00
51000 -382.73 -41.27 276.20 895.22 -4.68 -3.18 -30.31 0.02 367 0.30 0.69 0.76 0.87 0.40 0.30 1.00
52000 -368.42 -40.45 259.97 692.32 -4.31 -2.86 11.49 0.02 376 0.30 0.69 0.76 0.87 0.40 0.30 1.00
53000 -371.85 -45.88 496.40 1599.39 -4.58 -3.76 -48.66 0.02 327 0.30 0.69 0.76 0.87 0.41 0.30 1.00
54000 -360.76 -45.86 516.50 1401.02 -3.99 -4.33 -17.25 0.02 348 0.30 0.69 0.76 0.87 0.42 0.30 1.00
55000 -375.62 -43.18 353.74 1144.17 -4.11 -3.67 -12.50 0.02 342 0.30 0.69 0.76 0.87 0.42 0.30 1.00
56000 -359.55 -40.87 276.21 736.04 -4.53 -3.63 -41.55 0.02 375 0.30 0.69 0.76 0.87 0.43 0.30 1.00
57000 -372.44 -37.10 166.01 428.95 -4.64 -3.12 -21.01 0.02 348 0.30 0.69 0.76 0.87 0.44 0.30 1.00
58000 -372.11 -39.11 214.33 614.19 -4.17 -3.46 -6.37 0.02 346 0.30 0.69 0.76 0.87 0.44 0.30 1.00
59000 -381.21 -40.39 244.27 773.60 -4.26 -3.06 1.70 0.03 313 0.30 0.68 0.76 0.87 0.45 0.30 1.00
60000 -378.88 -38.93 209.30 569.08 -4.42 -2.85 8.91 0.03 350 0.30 0.68 0.75 0.87 0.46 0.30 1.00
61000 -376.40 -41.93 307.76 889.65 -4.54 -3.08 -11.80 0.03 338 0.30 0.68 0.76 0.87 0.47 0.30 1.00
62000 -376.76 -42.03 305.54 918.96 -4.59 -2.92 -21.48 0.03 364 0.30 0.68 0.76 0.87 0.47 0.30 1.00
63000 -379.17 -37.02 144.72 534.15 -4.58 -3.23 -15.82 0.04 353 0.30 0.68 0.76 0.87 0.48 0.30 1.00
64000 -378.55 -41.48 288.36 882.06 -4.34 -3.28 -0.25 0.02 336 0.30 0.68 0.76 0.86 0.49 0.30 1.00
65000 -362.49 -38.50 203.85 518.52 -4.13 -4.11 -23.33 0.02 346 0.30 0.68 0.76 0.86 0.49 0.30 1.00
66000 -365.48 -38.40 190.69 586.21 -4.13 -3.86 -10.61 0.02 371 0.30 0.68 0.76 0.87 0.49 0.30 1.00
67000 -364.32 -40.99 282.51 735.18 -4.24 -4.22 -38.53 0.02 348 0.30 0.68 0.76 0.86 0.50 0.30 1.00
68000 -382.53 -40.71 251.97 880.98 -3.99 -4.12 -15.81 0.02 336 0.30 0.68 0.76 0.86 0.50 0.30 1.00
69000 -382.21 -38.53 204.16 507.18 -3.74 -4.72 -7.78 0.01 391 0.30 0.68 0.76 0.87 0.51 0.30 1.00
70000 -383.41 -36.89 155.45 457.46 -3.67 -4.69 0.80 0.01 352 0.30 0.68 0.76 0.87 0.51 0.30 1.00
71000 -380.85 -42.06 314.00 913.77 -4.19 -4.04 -36.08 0.01 364 0.30 0.68 0.76 0.87 0.51 0.30 1.00
72000 -373.59 -40.62 269.69 691.31 -4.03 -3.64 -6.41 0.02 358 0.30 0.68 0.76 0.87 0.51 0.30 1.00
73000 -379.07 -39.96 237.56 701.38 -3.65 -4.49 -12.05 0.01 320 0.30 0.68 0.75 0.87 0.52 0.30 1.00
74000 -377.66 -43.77 395.69 1065.70 -3.73 -4.53 -4.12 0.01 363 0.30 0.68 0.76 0.87 0.52 0.30 1.00
75000 -370.45 -40.04 242.32 645.39 -3.43 -6.11 -43.30 0.01 347 0.30 0.68 0.76 0.87 0.52 0.30 1.00
76000 -377.27 -47.05 567.97 1690.90 -3.53 -6.41 -63.57 0.01 363 0.30 0.68 0.75 0.87 0.52 0.30 1.00
77000 -366.02 -43.33 374.21 924.31 -3.57 -6.38 -65.51 0.01 342 0.30 0.68 0.75 0.87 0.52 0.30 1.00
78000 -360.47 -43.30 367.16 1058.68 -3.94 -4.72 -32.27 0.01 371 0.30 0.69 0.75 0.87 0.52 0.30 1.00
79000 -381.72 -45.23 478.07 1215.66 -4.00 -4.79 -45.06 0.01 351 0.30 0.68 0.75 0.87 0.52 0.30 1.00
80000 -376.96 -42.57 331.11 1030.76 -4.06 -4.38 -32.17 0.02 313 0.30 0.68 0.75 0.87 0.52 0.30 1.00
81000 -373.18 -43.17 358.77 1081.93 -4.39 -4.14 -44.53 0.02 323 0.30 0.69 0.75 0.87 0.53 0.30 1.00
82000 -373.30 -42.37 327.67 904.87 -4.65 -3.11 -25.25 0.03 332 0.30 0.69 0.76 0.87 0.53 0.30 1.00
83000 -365.89 -44.86 468.57 1138.34 -4.36 -3.66 -17.71 0.02 348 0.30 0.69 0.75 0.87 0.54 0.30 1.00
84000 -373.96 -38.94 200.81 646.39 -4.31 -3.82 -15.32 0.03 327 0.30 0.69 0.75 0.87 0.54 0.30 1.00
85000 -375.05 -45.22 481.19 1249.34 -4.40 -4.01 -40.60 0.02 320 0.30 0.69 0.75 0.87 0.54 0.30 1.00
86000 -370.66 -40.28 254.21 682.40 -4.52 -3.08 -27.38 0.02 348 0.30 0.69 0.75 0.87 0.54 0.30 1.00
87000 -380.16 -44.26 403.60 1332.99 -4.00 -3.99 -9.59 0.02 352 0.30 0.69 0.76 0.87 0.55 0.30 1.00
88000 -372.67 -44.12 413.88 1111.28 -3.95 -4.51 -31.28 0.01 353 0.30 0.69 0.76 0.87 0.55 0.30 1.00
89000 -362.97 -42.44 343.69 860.74 -3.83 -4.30 -19.00 0.02 334 0.30 0.69 0.76 0.87 0.55 0.30 1.00
90000 -364.90 -40.98 274.37 789.07 -4.60 -3.65 -31.37 0.02 349 0.30 0.69 0.75 0.87 0.55 0.30 1.00
91000 -386.33 -37.88 180.07 525.91 -4.36 -3.81 -22.89 0.02 364 0.30 0.68 0.76 0.87 0.55 0.30 1.00
92000 -371.46 -40.02 253.15 612.96 -4.27 -4.28 -39.28 0.02 342 0.30 0.69 0.75 0.87 0.56 0.30 1.00
93000 -376.24 -40.88 278.62 723.87 -4.43 -3.46 -29.90 0.02 360 0.30 0.69 0.76 0.87 0.56 0.30 1.00
94000 -376.88 -41.13 283.18 754.37 -4.82 -2.89 -30.53 0.03 350 0.30 0.68 0.76 0.87 0.56 0.30 1.00
95000 -379.49 -38.59 207.14 517.65 -4.34 -4.05 -30.63 0.02 360 0.30 0.68 0.76 0.87 0.57 0.30 1.00
96000 -378.34 -40.07 247.62 664.86 -4.00 -3.86 -9.18 0.02 347 0.30 0.68 0.76 0.87 0.57 0.30 1.00
97000 -377.78 -40.73 264.90 767.02 -4.42 -3.99 -34.07 0.02 363 0.30 0.68 0.75 0.87 0.57 0.30 1.00
98000 -372.09 -43.61 404.30 943.27 -4.30 -3.93 -31.73 0.02 314 0.30 0.68 0.75 0.87 0.57 0.30 1.00
99000 -382.51 -37.84 172.64 575.76 -4.07 -4.01 -19.07 0.02 378 0.30 0.68 0.75 0.87 0.57 0.30 1.00
100000 -362.49 -40.93 284.79 697.20 -3.71 -4.56 -12.90 0.02 370 0.30 0.68 0.75 0.87 0.57 0.30 1.00
chainEV.sigmoid <- mymcmcEV.sigmoid$load()
chainEV.sigmoid <- set.burnin(chainEV.sigmoid, 0.3)
plot(chainEV.sigmoid)
strees.bayou <- Dtrees.bayou
mymcmcD.sigmoid$run(100000)
gen lnL prior alpha sig2 beta_le beta_ri beta_ce beta_sl k .alpha .beta_c .beta_l .beta_r .beta_s .sig2 .theta
1000 -187.80 -20.54 7.28 26.75 -0.99 -5.43 74.58 0.03 352 0.55 0.86 0.91 0.94 0.92 0.44 1.00
2000 -181.37 -20.44 5.93 32.81 -1.43 -4.70 68.02 0.02 402 0.49 0.85 0.92 0.93 0.91 0.44 1.00
3000 -175.73 -21.87 10.60 36.31 -1.71 -4.80 57.79 0.01 379 0.51 0.80 0.91 0.93 0.91 0.43 1.00
4000 -173.96 -22.08 12.31 30.61 -1.45 -5.50 63.03 0.01 412 0.52 0.81 0.90 0.92 0.88 0.42 1.00
5000 -176.10 -23.07 14.29 42.29 -1.10 -5.84 67.11 0.01 406 0.52 0.82 0.89 0.91 0.83 0.42 1.00
6000 -175.99 -22.90 12.23 38.43 -1.04 -6.38 79.77 0.01 370 0.50 0.82 0.88 0.91 0.79 0.41 1.00
7000 -179.85 -22.56 11.58 37.98 -1.38 -6.89 -17.55 0.01 395 0.51 0.83 0.88 0.91 0.76 0.41 1.00
8000 -172.07 -21.85 10.49 30.60 -1.70 -6.41 22.94 0.01 409 0.50 0.84 0.88 0.91 0.74 0.41 1.00
9000 -177.79 -22.77 11.40 35.47 -2.21 -5.91 -22.42 0.01 369 0.50 0.84 0.88 0.91 0.72 0.42 1.00
10000 -169.58 -24.14 16.34 45.72 -2.72 -5.34 -52.81 0.01 369 0.49 0.85 0.88 0.91 0.71 0.42 1.00
11000 -178.01 -21.53 8.10 34.70 -2.93 -5.08 -66.78 0.01 370 0.49 0.85 0.88 0.91 0.71 0.42 1.00
12000 -178.95 -22.94 11.40 48.84 -2.85 -5.12 -74.25 0.01 392 0.49 0.86 0.88 0.91 0.71 0.42 1.00
13000 -182.57 -21.06 8.36 30.95 -3.67 -4.12 -73.31 0.01 391 0.48 0.86 0.88 0.91 0.71 0.41 1.00
14000 -178.21 -23.26 12.84 38.76 -3.23 -4.88 -70.62 0.01 425 0.48 0.86 0.88 0.91 0.72 0.40 1.00
15000 -173.31 -22.27 11.35 33.88 -2.95 -4.95 -70.59 0.01 435 0.48 0.86 0.87 0.92 0.72 0.41 1.00
16000 -173.31 -23.78 15.03 48.36 -2.69 -4.88 -44.70 0.01 379 0.49 0.86 0.87 0.92 0.72 0.40 1.00
17000 -176.70 -23.14 10.78 32.63 -2.86 -5.51 -85.88 0.00 387 0.48 0.86 0.87 0.92 0.71 0.40 1.00
18000 -167.64 -23.86 16.33 46.26 -2.11 -6.19 -38.10 0.01 398 0.48 0.86 0.87 0.92 0.71 0.41 1.00
19000 -177.76 -24.18 14.73 53.16 -2.91 -6.14 -96.11 0.01 381 0.48 0.86 0.87 0.92 0.70 0.40 1.00
20000 -173.50 -21.50 8.60 30.19 -2.85 -5.62 -77.27 0.01 387 0.48 0.86 0.87 0.92 0.70 0.41 1.00
21000 -179.92 -21.30 6.56 26.38 -2.49 -5.33 22.24 0.00 386 0.48 0.86 0.87 0.92 0.69 0.41 1.00
22000 -172.45 -23.21 14.21 39.94 -2.16 -4.86 46.30 0.01 357 0.48 0.86 0.87 0.92 0.69 0.41 1.00
23000 -179.89 -22.62 9.21 34.79 -2.10 -5.61 38.06 0.00 407 0.48 0.86 0.87 0.92 0.69 0.41 1.00
24000 -175.21 -24.95 17.59 65.58 -1.41 -5.89 87.57 0.01 404 0.48 0.86 0.87 0.92 0.69 0.41 1.00
25000 -172.50 -22.92 13.52 32.79 -2.15 -5.65 18.62 0.01 381 0.47 0.86 0.87 0.92 0.69 0.41 1.00
26000 -174.52 -21.02 9.70 26.79 -1.76 -4.70 98.46 0.02 409 0.47 0.86 0.87 0.92 0.69 0.41 1.00
27000 -173.34 -23.32 17.21 40.14 -1.92 -4.48 94.30 0.01 424 0.47 0.86 0.87 0.92 0.69 0.41 1.00
28000 -170.67 -21.89 10.82 36.09 -2.28 -4.23 62.49 0.01 394 0.47 0.86 0.87 0.92 0.69 0.41 1.00
29000 -175.72 -22.14 11.27 36.20 -2.87 -4.26 -12.11 0.01 406 0.47 0.86 0.87 0.92 0.70 0.41 1.00
30000 -176.88 -20.66 6.82 25.70 -3.64 -3.91 -65.24 0.01 402 0.47 0.86 0.87 0.92 0.70 0.41 1.00
31000 -175.26 -24.43 19.43 58.88 -3.72 -3.21 -36.89 0.02 398 0.48 0.86 0.87 0.92 0.71 0.41 1.00
32000 -175.75 -25.71 24.57 72.73 -4.04 -2.38 -41.71 0.01 382 0.48 0.86 0.87 0.92 0.71 0.41 1.00
33000 -179.95 -19.67 6.09 21.05 -4.45 -2.11 -29.14 0.01 402 0.48 0.86 0.87 0.92 0.72 0.41 1.00
34000 -176.69 -21.65 10.08 34.89 -4.24 -2.47 -86.59 0.02 408 0.48 0.86 0.87 0.92 0.72 0.41 1.00
35000 -177.21 -21.73 12.26 28.87 -3.85 -3.33 -65.66 0.02 384 0.48 0.86 0.87 0.92 0.73 0.41 1.00
36000 -176.47 -22.04 12.43 31.92 -3.42 -3.74 -40.22 0.01 398 0.48 0.86 0.87 0.92 0.73 0.41 1.00
37000 -178.10 -20.49 8.00 25.36 -4.23 -3.27 -94.87 0.02 383 0.48 0.86 0.87 0.92 0.73 0.41 1.00
38000 -175.82 -21.80 9.63 37.97 -4.27 -2.94 -33.67 0.03 373 0.48 0.86 0.87 0.92 0.74 0.41 1.00
39000 -182.61 -20.93 9.00 25.25 -3.96 -1.84 5.74 0.03 381 0.47 0.86 0.87 0.92 0.74 0.41 1.00
40000 -177.90 -22.44 12.75 38.82 -4.57 -1.42 8.69 0.02 406 0.48 0.86 0.87 0.92 0.75 0.41 1.00
41000 -178.81 -21.02 7.54 31.99 -3.30 -2.82 36.12 0.01 398 0.48 0.87 0.87 0.92 0.75 0.41 1.00
42000 -169.39 -21.64 10.09 32.44 -3.40 -2.72 21.06 0.03 388 0.48 0.87 0.87 0.92 0.76 0.41 1.00
43000 -179.56 -20.59 7.16 29.67 -4.17 -2.27 -71.33 0.02 386 0.48 0.87 0.87 0.91 0.76 0.41 1.00
44000 -171.05 -24.07 17.71 54.51 -4.07 -2.69 -31.01 0.01 379 0.48 0.87 0.87 0.91 0.77 0.41 1.00
45000 -177.61 -23.22 14.67 43.13 -4.35 -1.69 -13.68 0.01 399 0.48 0.87 0.87 0.91 0.77 0.41 1.00
46000 -179.09 -22.56 12.45 41.55 -3.91 -2.35 -39.00 0.03 420 0.48 0.87 0.87 0.91 0.77 0.41 1.00
47000 -175.31 -22.15 11.93 36.72 -4.33 -2.48 -98.10 0.02 383 0.47 0.87 0.87 0.91 0.78 0.41 1.00
48000 -176.26 -23.26 16.27 38.07 -3.53 -3.12 2.03 0.01 406 0.47 0.87 0.87 0.91 0.78 0.41 1.00
49000 -177.20 -20.78 8.26 26.28 -4.08 -2.91 -53.23 0.01 392 0.47 0.87 0.87 0.92 0.78 0.41 1.00
50000 -174.13 -21.55 9.89 34.04 -3.16 -3.17 2.35 0.02 424 0.47 0.87 0.87 0.92 0.78 0.41 1.00
51000 -180.35 -21.11 7.15 35.58 -3.71 -3.46 -70.67 0.01 403 0.47 0.87 0.87 0.91 0.78 0.41 1.00
52000 -174.74 -22.96 12.60 39.16 -3.18 -4.55 -77.09 0.01 352 0.47 0.87 0.87 0.92 0.79 0.41 1.00
53000 -177.42 -20.85 8.42 26.06 -3.41 -3.40 8.36 0.01 392 0.48 0.87 0.87 0.91 0.79 0.41 1.00
54000 -177.54 -23.17 14.10 49.48 -4.01 -2.41 13.26 0.02 370 0.47 0.87 0.87 0.91 0.79 0.41 1.00
55000 -179.46 -20.77 8.50 26.87 -4.21 -2.79 -71.10 0.02 390 0.47 0.87 0.87 0.91 0.79 0.41 1.00
56000 -172.75 -24.40 21.62 48.76 -4.46 -2.04 -62.51 0.02 406 0.47 0.87 0.87 0.91 0.79 0.41 1.00
57000 -176.01 -28.61 42.50 106.79 -4.34 -2.03 -56.37 0.04 408 0.47 0.87 0.87 0.91 0.80 0.41 1.00
58000 -178.40 -26.55 30.86 72.05 -3.87 -2.22 -0.07 0.04 391 0.48 0.87 0.87 0.91 0.80 0.41 1.00
59000 -169.90 -22.59 13.58 37.54 -3.98 -2.48 -42.59 0.03 360 0.48 0.87 0.87 0.91 0.80 0.41 1.00
60000 -170.88 -24.27 20.41 49.33 -3.84 -2.84 -19.00 0.01 373 0.47 0.87 0.87 0.91 0.80 0.41 1.00
61000 -168.31 -23.34 18.05 38.30 -3.92 -2.72 -24.42 0.02 398 0.47 0.87 0.87 0.91 0.80 0.41 1.00
62000 -172.46 -26.22 28.38 79.84 -3.74 -3.36 -65.67 0.02 395 0.47 0.87 0.87 0.91 0.81 0.41 1.00
63000 -175.80 -22.50 11.70 38.47 -3.60 -3.62 -32.84 0.01 403 0.47 0.87 0.87 0.91 0.81 0.41 1.00
64000 -179.54 -23.08 14.37 42.07 -3.95 -2.80 -41.80 0.01 391 0.47 0.87 0.87 0.91 0.81 0.41 1.00
65000 -174.25 -21.92 10.74 37.27 -3.95 -2.79 -47.47 0.02 437 0.47 0.87 0.87 0.91 0.81 0.41 1.00
66000 -179.34 -20.14 5.75 24.07 -4.11 -2.41 -22.33 0.04 396 0.47 0.87 0.87 0.91 0.81 0.41 1.00
67000 -177.59 -25.23 20.62 57.23 -4.73 -2.18 -96.67 0.06 368 0.47 0.87 0.87 0.91 0.81 0.41 1.00
68000 -176.14 -24.10 16.85 37.94 -3.73 -2.00 19.81 0.07 388 0.47 0.87 0.87 0.91 0.82 0.41 1.00
69000 -172.37 -23.90 14.63 36.46 -4.11 -1.78 -17.61 0.09 360 0.47 0.87 0.87 0.91 0.82 0.41 1.00
70000 -179.17 -22.68 8.17 32.60 -4.06 -1.90 -35.89 0.11 386 0.47 0.87 0.87 0.91 0.82 0.41 1.00
71000 -174.69 -23.88 10.93 37.90 -4.76 -1.70 -92.56 0.13 415 0.47 0.87 0.87 0.91 0.82 0.41 1.00
72000 -174.99 -26.34 18.23 47.51 -4.46 -2.12 -77.35 0.17 384 0.47 0.87 0.87 0.91 0.83 0.41 1.00
73000 -179.07 -23.37 8.29 35.11 -3.90 -2.05 16.34 0.15 408 0.47 0.87 0.87 0.91 0.83 0.41 1.00
74000 -179.74 -23.26 7.56 34.47 -3.86 -1.84 84.80 0.15 415 0.47 0.87 0.87 0.91 0.83 0.41 1.00
75000 -176.60 -24.86 12.68 46.87 -4.24 -1.62 76.95 0.14 388 0.47 0.87 0.87 0.91 0.83 0.41 1.00
76000 -179.16 -24.79 12.21 44.33 -4.21 -1.79 59.65 0.15 420 0.47 0.87 0.87 0.91 0.83 0.41 1.00
77000 -176.77 -23.86 10.04 38.97 -4.94 -1.53 -89.77 0.14 372 0.47 0.87 0.87 0.91 0.84 0.41 1.00
78000 -177.98 -24.39 11.36 37.32 -4.86 -1.84 -85.18 0.16 425 0.47 0.87 0.87 0.91 0.84 0.41 1.00
79000 -179.28 -25.62 13.57 51.23 -4.82 -1.27 -41.03 0.17 386 0.47 0.87 0.87 0.91 0.84 0.41 1.00
80000 -169.34 -25.05 16.70 41.51 -4.44 -1.47 -33.89 0.12 406 0.47 0.87 0.87 0.91 0.84 0.41 1.00
81000 -174.14 -24.97 14.75 39.06 -4.64 -2.31 -90.69 0.15 383 0.47 0.87 0.87 0.91 0.84 0.41 1.00
82000 -173.97 -25.19 16.43 40.99 -4.64 -1.43 -75.79 0.13 404 0.47 0.87 0.87 0.91 0.85 0.41 1.00
83000 -178.64 -25.36 16.87 45.27 -5.03 -1.24 -35.80 0.13 381 0.47 0.87 0.87 0.91 0.85 0.41 1.00
84000 -181.50 -21.63 7.54 23.15 -4.93 -1.29 -55.76 0.10 368 0.47 0.87 0.87 0.91 0.85 0.41 1.00
85000 -178.57 -22.21 7.23 29.82 -4.90 -1.03 16.26 0.11 411 0.47 0.87 0.87 0.91 0.85 0.41 1.00
86000 -180.93 -23.05 9.74 32.46 -4.96 -0.71 -95.12 0.11 392 0.47 0.87 0.87 0.91 0.85 0.41 1.00
87000 -177.47 -22.23 8.45 29.48 -4.83 -1.24 -27.34 0.09 366 0.47 0.88 0.87 0.91 0.85 0.41 1.00
88000 -179.32 -22.10 9.34 28.33 -3.90 -2.42 2.58 0.08 437 0.47 0.88 0.87 0.91 0.85 0.41 1.00
89000 -179.70 -21.91 7.94 35.35 -5.06 -1.78 -86.55 0.06 399 0.47 0.88 0.87 0.91 0.86 0.41 1.00
90000 -178.87 -21.19 6.80 23.69 -5.14 -1.17 -59.60 0.08 400 0.47 0.88 0.87 0.91 0.86 0.41 1.00
91000 -179.25 -21.67 7.50 27.80 -4.57 -1.43 -61.86 0.08 387 0.47 0.88 0.87 0.91 0.86 0.41 1.00
92000 -178.81 -22.12 6.35 30.40 -5.25 -1.35 -54.38 0.12 390 0.47 0.88 0.87 0.91 0.86 0.41 1.00
93000 -175.21 -24.36 12.69 36.54 -4.14 -1.87 -10.89 0.14 392 0.47 0.88 0.87 0.91 0.86 0.41 1.00
94000 -176.99 -24.51 11.50 47.70 -4.27 -1.95 -20.92 0.13 398 0.47 0.88 0.87 0.91 0.86 0.41 1.00
95000 -177.31 -23.96 9.96 38.29 -4.49 -1.83 21.87 0.15 372 0.47 0.88 0.87 0.91 0.86 0.41 1.00
96000 -174.08 -26.88 20.24 60.30 -4.21 -1.98 17.57 0.16 366 0.47 0.88 0.87 0.91 0.87 0.41 1.00
97000 -178.95 -23.97 9.82 33.18 -3.89 -1.81 2.44 0.17 386 0.47 0.88 0.87 0.91 0.87 0.41 1.00
98000 -179.14 -24.44 9.68 38.19 -4.91 -1.62 -79.79 0.19 403 0.47 0.88 0.87 0.91 0.87 0.41 1.00
99000 -178.56 -25.81 13.96 52.65 -4.69 -1.55 -70.47 0.17 366 0.47 0.88 0.87 0.91 0.87 0.41 1.00
100000 -172.91 -23.74 9.55 30.21 -4.19 -2.49 -81.92 0.17 375 0.47 0.88 0.87 0.91 0.87 0.41 1.00
chainD.sigmoid <- mymcmcD.sigmoid$load()
chainD.sigmoid <- set.burnin(chainD.sigmoid, 0.3)
plot(chainD.sigmoid)
strees.bayou <- EVtrees.bayou
mymcmcEV.linear$run(100000)
gen lnL prior alpha sig2 beta_in beta_sl k .alpha .beta_i .beta_s .sig2 .theta
1000 -375.69 -32.60 257.03 687.85 -5.79 0.01 307 0.41 0.63 0.67 0.35 1.00
2000 -366.95 -32.65 259.55 693.37 -6.00 0.01 375 0.37 0.55 0.60 0.35 1.00
3000 -370.39 -35.98 384.68 1173.67 -5.85 0.01 348 0.35 0.54 0.58 0.30 1.00
4000 -379.18 -33.03 263.89 797.83 -6.00 0.01 353 0.34 0.52 0.55 0.29 1.00
5000 -369.36 -34.32 314.83 922.01 -5.84 0.01 347 0.33 0.51 0.55 0.29 1.00
6000 -377.09 -31.86 235.75 604.47 -5.83 0.01 323 0.33 0.51 0.55 0.30 1.00
7000 -366.39 -37.61 485.26 1313.04 -5.86 0.01 325 0.32 0.51 0.55 0.30 1.00
8000 -381.82 -31.29 208.55 635.44 -5.99 0.01 367 0.31 0.50 0.55 0.30 1.00
9000 -378.95 -29.93 174.32 514.12 -5.87 0.01 323 0.31 0.50 0.55 0.30 1.00
10000 -370.47 -34.77 333.27 980.12 -5.87 0.01 348 0.31 0.50 0.55 0.31 1.00
11000 -368.83 -35.64 387.70 969.08 -6.01 0.01 349 0.30 0.50 0.54 0.31 1.00
12000 -380.82 -30.08 179.91 512.32 -5.98 0.01 347 0.30 0.50 0.54 0.30 1.00
13000 -381.53 -33.43 276.08 857.68 -5.97 0.01 367 0.30 0.50 0.54 0.31 1.00
14000 -373.77 -35.85 395.21 1019.63 -5.92 0.01 357 0.30 0.49 0.54 0.31 1.00
15000 -371.95 -30.93 206.70 541.97 -5.84 0.01 368 0.30 0.49 0.54 0.30 1.00
16000 -383.06 -32.76 257.61 746.48 -5.88 0.01 351 0.30 0.49 0.54 0.30 1.00
17000 -375.04 -34.95 333.10 1073.25 -5.81 0.01 323 0.30 0.49 0.54 0.30 1.00
18000 -377.12 -30.51 188.54 559.75 -5.85 0.01 369 0.31 0.49 0.53 0.30 1.00
19000 -369.89 -37.44 462.38 1396.61 -5.83 0.01 332 0.30 0.49 0.53 0.30 1.00
20000 -382.44 -28.34 140.40 402.00 -6.02 0.01 358 0.30 0.49 0.53 0.30 1.00
21000 -379.90 -31.46 221.08 592.00 -5.85 0.01 378 0.30 0.49 0.53 0.30 1.00
22000 -376.02 -31.30 218.71 560.95 -5.89 0.01 360 0.30 0.48 0.53 0.30 1.00
23000 -374.49 -36.92 441.28 1243.68 -6.04 0.01 338 0.30 0.49 0.53 0.30 1.00
24000 -375.98 -34.67 335.43 911.70 -5.84 0.01 367 0.30 0.49 0.53 0.30 1.00
25000 -372.27 -30.53 182.06 621.86 -5.96 0.01 332 0.30 0.49 0.53 0.30 1.00
26000 -368.04 -33.34 282.10 770.80 -5.88 0.01 314 0.30 0.49 0.53 0.30 1.00
27000 -368.68 -39.44 609.56 1595.81 -5.80 0.01 332 0.30 0.49 0.53 0.30 1.00
28000 -366.47 -33.90 301.38 847.67 -5.87 0.01 347 0.30 0.49 0.52 0.30 1.00
29000 -373.25 -37.09 451.38 1263.30 -5.94 0.01 357 0.30 0.49 0.52 0.30 1.00
30000 -389.32 -31.05 198.31 643.29 -5.92 0.01 310 0.30 0.49 0.52 0.30 1.00
31000 -362.78 -35.10 348.89 1011.10 -5.94 0.01 311 0.30 0.49 0.52 0.30 1.00
32000 -368.14 -33.12 287.81 651.10 -5.81 0.01 334 0.30 0.49 0.52 0.30 1.00
33000 -379.40 -33.66 275.14 972.39 -5.91 0.01 347 0.30 0.49 0.52 0.30 1.00
34000 -374.01 -32.37 236.45 777.70 -5.88 0.01 368 0.30 0.49 0.52 0.30 1.00
35000 -371.21 -31.57 219.12 639.09 -5.87 0.01 330 0.30 0.49 0.52 0.30 1.00
36000 -380.42 -34.11 295.82 995.19 -5.96 0.01 375 0.30 0.49 0.52 0.30 1.00
37000 -375.00 -31.24 204.49 652.18 -5.90 0.01 347 0.30 0.49 0.52 0.30 1.00
38000 -374.40 -32.50 265.40 599.71 -5.77 0.01 368 0.30 0.49 0.52 0.30 1.00
39000 -382.89 -29.01 145.64 511.42 -5.85 0.01 357 0.30 0.49 0.52 0.30 1.00
40000 -367.62 -35.21 362.79 951.39 -6.05 0.01 342 0.30 0.48 0.52 0.30 1.00
41000 -382.53 -31.72 220.40 675.94 -5.72 0.01 336 0.30 0.48 0.52 0.30 1.00
42000 -382.23 -31.78 225.07 662.61 -6.09 0.01 336 0.30 0.48 0.53 0.30 1.00
43000 -377.17 -33.00 273.90 704.71 -5.82 0.01 357 0.30 0.49 0.53 0.30 1.00
44000 -375.87 -29.80 172.61 495.82 -5.88 0.01 350 0.30 0.48 0.53 0.30 1.00
45000 -374.75 -32.52 256.73 666.34 -5.87 0.01 350 0.30 0.48 0.53 0.30 1.00
46000 -373.71 -34.25 328.44 787.57 -6.07 0.01 330 0.30 0.48 0.53 0.30 1.00
47000 -374.73 -35.95 383.05 1171.61 -5.99 0.01 350 0.30 0.48 0.53 0.30 1.00
48000 -367.66 -36.22 401.77 1159.16 -5.78 0.01 348 0.30 0.48 0.53 0.30 1.00
49000 -369.72 -33.54 294.28 754.66 -5.87 0.01 347 0.30 0.48 0.53 0.30 1.00
50000 -382.87 -32.50 249.68 711.46 -5.87 0.01 363 0.30 0.48 0.53 0.30 1.00
51000 -384.55 -34.71 339.83 898.56 -5.89 0.01 313 0.30 0.48 0.53 0.30 1.00
52000 -378.58 -30.57 186.96 592.08 -5.90 0.01 314 0.30 0.48 0.53 0.30 1.00
53000 -379.79 -30.89 197.31 600.22 -5.88 0.01 314 0.30 0.48 0.53 0.30 1.00
54000 -374.31 -33.60 280.72 891.50 -6.00 0.01 358 0.30 0.48 0.53 0.30 1.00
55000 -384.84 -34.71 333.45 948.97 -5.93 0.01 333 0.30 0.48 0.53 0.30 1.00
56000 -371.33 -31.87 232.07 636.28 -5.83 0.01 347 0.30 0.48 0.53 0.30 1.00
57000 -378.38 -30.27 184.82 520.77 -5.75 0.01 327 0.30 0.48 0.53 0.30 1.00
58000 -375.30 -30.05 172.49 561.55 -6.06 0.01 325 0.30 0.48 0.53 0.30 1.00
59000 -366.62 -38.39 519.00 1570.93 -5.81 0.01 365 0.30 0.48 0.53 0.30 1.00
60000 -367.25 -35.17 366.86 900.51 -5.91 0.01 348 0.30 0.48 0.53 0.30 1.00
61000 -380.20 -36.12 389.95 1198.36 -5.72 0.01 353 0.30 0.48 0.53 0.30 1.00
62000 -377.69 -34.17 314.07 861.45 -5.96 0.01 353 0.30 0.48 0.53 0.30 1.00
63000 -380.99 -35.02 348.89 967.98 -5.79 0.01 326 0.30 0.48 0.53 0.30 1.00
64000 -373.84 -33.48 281.60 828.20 -5.74 0.01 329 0.30 0.48 0.53 0.30 1.00
65000 -372.40 -38.43 540.16 1418.66 -5.86 0.01 364 0.30 0.48 0.53 0.30 1.00
66000 -384.31 -31.04 199.78 625.14 -5.73 0.01 350 0.30 0.48 0.53 0.30 1.00
67000 -375.68 -32.03 229.40 712.58 -5.97 0.01 348 0.30 0.48 0.53 0.30 1.00
68000 -379.97 -32.51 241.54 786.40 -5.92 0.01 347 0.30 0.48 0.53 0.30 1.00
69000 -374.66 -34.73 332.71 961.36 -5.79 0.01 314 0.30 0.48 0.53 0.30 1.00
70000 -379.11 -36.26 399.58 1208.48 -5.92 0.01 367 0.30 0.48 0.53 0.30 1.00
71000 -373.87 -33.84 300.11 831.56 -5.95 0.01 358 0.30 0.48 0.53 0.30 1.00
72000 -384.95 -31.35 211.75 628.34 -5.84 0.01 342 0.30 0.48 0.53 0.30 1.00
73000 -377.22 -32.50 248.54 723.25 -5.89 0.01 357 0.30 0.48 0.53 0.30 1.00
74000 -378.22 -33.14 280.06 715.08 -5.91 0.01 378 0.30 0.48 0.53 0.30 1.00
75000 -367.93 -36.18 391.33 1238.00 -5.91 0.01 346 0.30 0.48 0.53 0.30 1.00
76000 -386.79 -32.04 221.68 779.72 -5.78 0.01 336 0.30 0.48 0.53 0.30 1.00
77000 -370.74 -31.18 219.70 522.45 -5.86 0.01 348 0.30 0.48 0.53 0.30 1.00
78000 -366.94 -42.26 863.07 2087.46 -5.88 0.01 314 0.30 0.48 0.53 0.30 1.00
79000 -380.22 -36.06 405.41 1046.60 -5.84 0.01 320 0.30 0.48 0.53 0.30 1.00
80000 -374.16 -32.58 253.40 709.34 -5.77 0.01 324 0.30 0.48 0.53 0.30 1.00
81000 -381.50 -34.18 320.51 817.15 -5.92 0.01 336 0.30 0.48 0.53 0.30 1.00
82000 -383.02 -31.47 209.96 683.45 -6.05 0.01 367 0.30 0.48 0.53 0.30 1.00
83000 -373.13 -33.12 268.86 792.02 -5.92 0.01 376 0.30 0.48 0.53 0.30 1.00
84000 -376.62 -31.29 209.92 623.25 -5.88 0.01 369 0.30 0.48 0.53 0.30 1.00
85000 -372.13 -30.72 202.01 521.35 -5.97 0.01 349 0.30 0.48 0.53 0.30 1.00
86000 -374.03 -34.24 330.26 771.61 -5.96 0.01 317 0.30 0.48 0.53 0.30 1.00
87000 -375.82 -33.25 278.00 766.59 -5.77 0.01 314 0.30 0.48 0.53 0.30 1.00
88000 -376.60 -32.75 259.24 729.83 -5.89 0.01 361 0.30 0.48 0.53 0.30 1.00
89000 -380.50 -32.88 260.44 767.08 -5.95 0.01 348 0.30 0.48 0.53 0.30 1.00
90000 -386.14 -30.97 210.42 528.42 -5.84 0.01 367 0.30 0.48 0.53 0.30 1.00
91000 -385.05 -34.13 312.48 856.09 -5.93 0.01 336 0.30 0.48 0.53 0.30 1.00
92000 -387.15 -29.50 164.41 481.90 -5.84 0.01 348 0.30 0.48 0.53 0.30 1.00
93000 -374.98 -36.95 435.42 1316.14 -5.91 0.01 369 0.30 0.48 0.53 0.30 1.00
94000 -382.91 -30.54 190.69 551.82 -5.97 0.01 375 0.30 0.48 0.53 0.30 1.00
95000 -370.75 -30.28 186.87 511.70 -5.95 0.01 348 0.30 0.48 0.53 0.30 1.00
96000 -376.11 -33.27 264.92 888.97 -6.11 0.01 351 0.30 0.48 0.53 0.30 1.00
97000 -381.73 -31.46 223.78 572.28 -5.94 0.01 378 0.30 0.48 0.53 0.30 1.00
98000 -383.43 -34.52 326.61 916.56 -5.96 0.01 321 0.30 0.48 0.53 0.30 1.00
99000 -373.88 -35.73 380.98 1065.74 -5.88 0.01 314 0.30 0.48 0.53 0.30 1.00
100000 -376.88 -31.83 233.21 615.43 -5.89 0.01 350 0.30 0.48 0.53 0.30 1.00
chainEV.linear <- mymcmcEV.linear$load()
chainEV.linear <- set.burnin(chainEV.linear, 0.3)
plot(chainEV.linear)
strees.bayou <- Dtrees.bayou
mymcmcD.linear$run(100000)
gen lnL prior alpha sig2 beta_in beta_sl k .alpha .beta_i .beta_s .sig2 .theta
1000 -174.14 -14.71 15.34 29.65 -5.38 0.01 388 0.50 0.73 0.65 0.44 1.00
2000 -173.79 -15.04 12.30 42.92 -4.93 0.01 435 0.46 0.70 0.63 0.40 1.00
3000 -172.51 -14.35 11.52 34.19 -5.09 0.01 372 0.45 0.70 0.65 0.39 1.00
4000 -171.20 -14.95 13.76 36.00 -4.99 0.01 424 0.45 0.69 0.64 0.38 1.00
5000 -176.05 -15.29 13.41 43.56 -4.93 0.01 385 0.46 0.68 0.63 0.37 1.00
6000 -178.46 -12.94 6.76 25.68 -4.51 0.01 406 0.47 0.68 0.63 0.37 1.00
7000 -175.65 -14.74 12.02 41.38 -5.38 0.01 380 0.46 0.68 0.64 0.38 1.00
8000 -175.56 -13.69 10.04 28.28 -4.98 0.01 437 0.46 0.68 0.64 0.38 1.00
9000 -178.13 -14.42 10.25 41.08 -5.15 0.01 381 0.45 0.68 0.64 0.38 1.00
10000 -173.51 -14.37 11.57 34.91 -5.20 0.01 369 0.45 0.69 0.65 0.39 1.00
11000 -172.69 -14.83 12.70 37.46 -4.98 0.01 402 0.45 0.69 0.65 0.39 1.00
12000 -174.88 -15.28 13.12 45.92 -5.06 0.01 380 0.45 0.69 0.66 0.39 1.00
13000 -175.76 -14.94 13.38 36.81 -4.94 0.01 372 0.45 0.69 0.66 0.39 1.00
14000 -177.03 -14.01 10.74 29.81 -4.87 0.01 383 0.45 0.68 0.66 0.39 1.00
15000 -172.72 -19.82 34.32 103.64 -5.11 0.01 412 0.46 0.68 0.66 0.39 1.00
16000 -181.03 -15.23 14.56 36.86 -4.83 0.01 399 0.46 0.68 0.66 0.39 1.00
17000 -178.67 -16.31 16.12 58.66 -5.09 0.01 366 0.46 0.69 0.65 0.39 1.00
18000 -179.81 -12.82 6.30 24.16 -4.31 0.01 404 0.46 0.69 0.66 0.40 1.00
19000 -175.07 -15.46 15.35 39.91 -4.97 0.01 379 0.46 0.69 0.66 0.40 1.00
20000 -177.94 -13.81 9.71 32.18 -5.13 0.01 369 0.46 0.69 0.66 0.40 1.00
21000 -175.49 -15.10 13.64 39.73 -5.05 0.01 396 0.46 0.69 0.66 0.40 1.00
22000 -166.27 -17.32 24.68 52.39 -5.20 0.01 388 0.46 0.69 0.66 0.39 1.00
23000 -182.43 -17.01 18.43 63.12 -4.73 0.01 391 0.46 0.69 0.66 0.40 1.00
24000 -171.77 -18.86 33.63 67.93 -5.25 0.01 391 0.46 0.69 0.66 0.40 1.00
25000 -177.61 -14.75 11.03 44.84 -5.20 0.01 387 0.46 0.69 0.66 0.40 1.00
26000 -176.94 -12.73 7.85 23.36 -5.08 0.01 398 0.46 0.69 0.66 0.40 1.00
27000 -174.22 -18.00 25.87 67.89 -5.16 0.01 380 0.46 0.69 0.66 0.40 1.00
28000 -175.38 -14.81 12.59 38.35 -5.06 0.01 395 0.46 0.69 0.66 0.40 1.00
29000 -179.72 -15.68 15.08 49.16 -5.33 0.00 407 0.46 0.69 0.66 0.40 1.00
30000 -176.36 -14.80 12.18 41.26 -5.27 0.01 409 0.46 0.69 0.66 0.40 1.00
31000 -172.62 -15.33 13.78 45.17 -5.18 0.01 404 0.46 0.69 0.66 0.40 1.00
32000 -171.11 -15.47 15.33 41.58 -5.12 0.01 366 0.46 0.69 0.66 0.40 1.00
33000 -174.75 -14.26 9.72 38.54 -4.93 0.01 402 0.46 0.69 0.66 0.40 1.00
34000 -175.51 -20.72 39.62 117.07 -4.82 0.01 387 0.46 0.69 0.66 0.40 1.00
35000 -178.46 -12.84 7.76 25.57 -5.21 0.01 370 0.47 0.69 0.66 0.40 1.00
36000 -175.46 -13.60 8.00 34.72 -5.00 0.01 372 0.47 0.69 0.66 0.40 1.00
37000 -174.86 -15.11 12.61 45.05 -5.14 0.01 396 0.47 0.69 0.66 0.40 1.00
38000 -179.89 -12.95 7.76 27.74 -5.38 0.00 403 0.47 0.69 0.66 0.40 1.00
39000 -177.68 -13.89 9.53 31.47 -4.78 0.01 387 0.47 0.69 0.66 0.40 1.00
40000 -179.80 -13.18 9.28 23.82 -4.95 0.01 374 0.47 0.69 0.66 0.40 1.00
41000 -175.59 -14.32 11.19 32.27 -4.77 0.01 404 0.46 0.69 0.66 0.40 1.00
42000 -177.91 -18.77 30.69 77.14 -5.34 0.01 392 0.46 0.69 0.66 0.40 1.00
43000 -176.86 -13.94 9.31 34.64 -4.96 0.01 383 0.46 0.69 0.66 0.40 1.00
44000 -178.40 -15.13 13.53 43.31 -5.39 0.01 399 0.46 0.69 0.66 0.40 1.00
45000 -168.49 -15.13 14.34 37.01 -4.96 0.01 366 0.46 0.69 0.66 0.40 1.00
46000 -174.16 -16.95 20.30 58.24 -5.12 0.01 368 0.46 0.69 0.66 0.40 1.00
47000 -175.05 -15.31 14.18 43.73 -5.27 0.01 406 0.46 0.69 0.66 0.40 1.00
48000 -175.41 -16.64 18.63 54.79 -4.95 0.01 415 0.46 0.69 0.66 0.40 1.00
49000 -175.81 -14.03 10.14 34.81 -5.24 0.01 415 0.46 0.69 0.66 0.40 1.00
50000 -177.80 -13.23 7.41 32.29 -5.15 0.01 408 0.46 0.69 0.66 0.40 1.00
51000 -174.49 -14.98 13.61 37.65 -5.04 0.01 437 0.46 0.69 0.66 0.40 1.00
52000 -176.22 -14.71 11.91 39.92 -5.17 0.01 387 0.46 0.69 0.66 0.40 1.00
53000 -173.55 -14.42 12.43 32.53 -5.14 0.01 403 0.46 0.69 0.66 0.40 1.00
54000 -178.08 -16.07 18.76 41.57 -5.06 0.01 372 0.46 0.69 0.66 0.40 1.00
55000 -176.59 -13.84 9.26 35.08 -5.24 0.01 411 0.46 0.69 0.66 0.40 1.00
56000 -171.58 -17.88 24.61 66.40 -4.95 0.01 372 0.46 0.69 0.66 0.40 1.00
57000 -174.79 -14.48 10.46 35.49 -4.55 0.01 408 0.46 0.69 0.66 0.40 1.00
58000 -180.79 -14.02 9.07 38.49 -5.11 0.01 407 0.46 0.69 0.66 0.40 1.00
59000 -178.81 -14.82 12.12 39.86 -5.01 0.01 369 0.46 0.69 0.66 0.40 1.00
60000 -173.84 -14.19 9.79 36.69 -4.91 0.01 399 0.47 0.69 0.66 0.40 1.00
61000 -176.48 -15.29 13.10 48.30 -5.29 0.01 374 0.47 0.69 0.66 0.40 1.00
62000 -176.46 -16.18 16.75 53.07 -5.18 0.01 385 0.47 0.69 0.66 0.40 1.00
63000 -173.85 -15.69 14.10 46.71 -4.69 0.01 380 0.47 0.69 0.66 0.40 1.00
64000 -169.73 -15.74 17.02 39.74 -4.96 0.01 388 0.47 0.69 0.66 0.40 1.00
65000 -177.04 -15.29 12.69 44.76 -4.77 0.01 393 0.47 0.69 0.66 0.40 1.00
66000 -177.82 -15.95 17.01 47.05 -5.26 0.01 381 0.46 0.69 0.66 0.40 1.00
67000 -179.80 -14.46 11.31 37.61 -5.19 0.00 426 0.47 0.69 0.66 0.40 1.00
68000 -178.54 -14.49 11.53 37.63 -5.24 0.00 423 0.47 0.69 0.66 0.40 1.00
69000 -173.12 -15.14 14.39 36.58 -4.90 0.01 379 0.47 0.69 0.66 0.40 1.00
70000 -176.81 -15.27 13.30 45.06 -5.08 0.01 399 0.47 0.69 0.66 0.40 1.00
71000 -169.43 -16.99 20.11 56.87 -4.86 0.01 383 0.46 0.69 0.66 0.40 1.00
72000 -178.33 -17.24 20.58 64.82 -5.03 0.01 423 0.46 0.69 0.65 0.40 1.00
73000 -176.30 -13.17 8.31 27.26 -5.03 0.01 395 0.47 0.69 0.65 0.40 1.00
74000 -175.09 -18.53 28.39 73.29 -4.99 0.01 368 0.46 0.69 0.65 0.40 1.00
75000 -169.79 -16.89 21.07 50.26 -4.85 0.01 423 0.46 0.69 0.65 0.40 1.00
76000 -175.25 -14.14 9.93 36.13 -5.01 0.01 352 0.46 0.69 0.65 0.40 1.00
77000 -171.12 -16.08 18.38 41.30 -4.86 0.01 395 0.46 0.69 0.65 0.40 1.00
78000 -174.80 -13.35 9.13 26.07 -4.91 0.01 404 0.46 0.69 0.65 0.40 1.00
79000 -179.75 -13.30 8.21 31.52 -5.47 0.01 404 0.46 0.69 0.65 0.40 1.00
80000 -172.72 -17.58 23.02 61.27 -4.82 0.01 372 0.47 0.69 0.65 0.40 1.00
81000 -173.91 -15.31 15.78 32.93 -4.67 0.01 388 0.47 0.69 0.65 0.40 1.00
82000 -172.69 -15.66 14.91 43.81 -4.78 0.01 395 0.47 0.69 0.65 0.40 1.00
83000 -175.70 -15.96 15.35 53.87 -5.19 0.01 420 0.47 0.69 0.65 0.40 1.00
84000 -171.35 -15.44 14.90 43.00 -5.16 0.01 408 0.47 0.69 0.65 0.40 1.00
85000 -173.64 -15.67 14.90 46.30 -4.97 0.01 396 0.47 0.69 0.65 0.40 1.00
86000 -178.07 -15.79 15.28 50.06 -5.24 0.01 411 0.47 0.69 0.65 0.40 1.00
87000 -180.07 -12.44 6.66 23.81 -5.09 0.01 399 0.47 0.69 0.65 0.40 1.00
88000 -174.67 -18.98 26.84 98.73 -4.91 0.01 415 0.47 0.69 0.65 0.40 1.00
89000 -176.27 -15.24 14.38 38.91 -4.95 0.01 385 0.47 0.69 0.65 0.40 1.00
90000 -174.45 -16.36 17.52 52.90 -5.03 0.01 391 0.47 0.69 0.65 0.40 1.00
91000 -171.66 -16.12 17.17 45.22 -4.78 0.01 381 0.47 0.68 0.65 0.40 1.00
92000 -172.61 -13.25 8.22 28.63 -5.05 0.01 390 0.47 0.68 0.65 0.40 1.00
93000 -184.38 -12.07 4.54 23.08 -4.45 0.01 391 0.47 0.68 0.65 0.40 1.00
94000 -176.00 -13.45 7.99 33.05 -5.12 0.01 404 0.47 0.68 0.65 0.41 1.00
95000 -178.27 -14.75 11.77 42.98 -5.43 0.01 391 0.47 0.69 0.65 0.41 1.00
96000 -169.50 -14.74 11.60 38.79 -4.84 0.01 394 0.47 0.69 0.65 0.41 1.00
97000 -176.12 -15.31 13.79 42.61 -4.95 0.01 392 0.46 0.68 0.65 0.41 1.00
98000 -167.91 -15.73 16.69 38.65 -4.76 0.01 366 0.47 0.68 0.65 0.40 1.00
99000 -176.85 -15.34 12.79 45.61 -4.77 0.01 406 0.47 0.68 0.65 0.40 1.00
100000 -174.30 -13.25 8.37 28.71 -5.14 0.01 382 0.47 0.68 0.65 0.40 1.00
chainD.linear <- mymcmcD.linear$load()
chainD.linear <- set.burnin(chainD.linear, 0.3)
plot(chainD.linear)
optimizeModelsOverSimmap <- function(i, strees.bayou=strees.bayou, chain.sigmoid=chain.sigmoid, chain.linear=chain.linear, chain.step=chain.step, cache = cache){
sb <- strees.bayou[[i]]$sb
t2 <- strees.bayou[[i]]$t2
loc <- strees.bayou[[i]]$loc
k <- length(sb)
ntheta <- ncat - 1
lik.sigmoid <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[6] * (midbins - pp[5]))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.sigmoid0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
beta_slope = pp[5],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]/(1 + exp(pp[5] * (midbins - 0))),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.tanhLin <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
beta_slope = pp[6],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[6]*midbins + pp[4]*tanh(midbins - pp[5]),
sb = sb,
loc=loc,
t2=t2)
model.sigmoidTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.sigmoid <- pull.pars(length(chain.sigmoid$gen), chain.sigmoid, model.sigmoidTheta)
pp.sigmoid <- unname(c(pars.sigmoid$alpha, pars.sigmoid$sig2, pars.sigmoid$beta_left, pars.sigmoid$beta_right, pars.sigmoid$beta_center, pars.sigmoid$beta_slope))
pp.sigmoid0 <- pp.sigmoid[-5]
lik.step <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = pp[5],
k = k,
ntheta=ntheta,
theta = ifelse(midbins>pp[5], pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
lik.step0 <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_left = pp[3],
beta_right = pp[4],
beta_center = 0,
k = k,
ntheta=ntheta,
theta = ifelse(midbins>0, pp[4], pp[3]),
sb = sb,
loc=loc,
t2=t2)
model.stepTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.step <- pull.pars(length(chain.step$gen), chain.step, model.stepTheta)
pp.step <- unname(c(pars.step$alpha, pars.step$sig2, pars.step$beta_left, pars.step$beta_right, pars.step$beta_center))
pp.step0 <- pp.step[-5]
lik.linear <- function(pp){
pars <- list(alpha = pp[1],
sig2 = pp[2],
beta_intercept = pp[3],
beta_slope = pp[4],
k = k,
ntheta=ntheta,
theta = pp[3] + pp[4]*midbins,
sb = sb,
loc=loc,
t2=t2)
model.linearTheta$lik.fn(pars, cache, cache$dat)$loglik[,1]
}
## Parameter order: alpha, sig2, beta_left, beta_right, beta_center, beta_slope
pars.linear <- pull.pars(length(chain.linear$gen), chain.linear, model.linearTheta)
pp.linear <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, pars.linear$beta_slope))
pp.tanhLin <- unname(c(pars.linear$alpha, pars.linear$sig2, pars.linear$beta_intercept, rnorm(1), runif(1, -10, 10), pars.linear$beta_slope))
opt.sigmoid <- optimx(pp.sigmoid, lik.sigmoid, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200, -0.1),
upper=c(2000, 2000, 0, 0, 200, 10))
opt.step <- optimx(pp.step, lik.step, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-200),
upper=c(2000, 2000, 0, 0, 200))
opt.sigmoid0 <- optimx(pp.sigmoid0, lik.sigmoid0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12,-0.1),
upper=c(2000, 2000, 0, 0, 10))
opt.step0 <- optimx(pp.step0, lik.step0, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-12),
upper=c(2000, 2000, 0, 0))
opt.linear <- optimx(pp.linear, lik.linear, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12,-1),
upper=c(2000, 2000, 0, 1))
opt.tanhLin <- optimx(pp.tanhLin, lik.tanhLin, control=list(maximize=TRUE), method=c('nlminb'), lower=c(0,0,-12, -5,-200, -1), upper=c(2000, 2000, 0, 5, 200, 1))
aics <- list()
aics[['aic.sigmoid']] <- 2*6 - 2*opt.sigmoid$value
aics[['aic.step']] <- 2*5 - 2*opt.step$value
aics[['aic.linear']] <- 2*4 - 2*opt.linear$value
aics[['aic.sigmoid0']] <- 2*5 - 2*opt.sigmoid0$value
aics[['aic.step0']] <- 2*4 - 2*opt.step0$value
aics[['aic.tanhLin']] <- 2*6 - 2*opt.tanhLin$value
aics
return(list(opt.sigmoid=opt.sigmoid, opt.linear=opt.linear, opt.step=opt.step, opt.sigmoid0=opt.sigmoid0, opt.step0=opt.step0, opt.tanhLin=opt.tanhLin, aics=aics))
}
require(optimx)
cacheEV <- bayou:::.prepare.ou.univariate(tdEV$phy, tdEV[['lnVs']], SE=0.02, pred=tdEV['binpred'])
cacheD <- bayou:::.prepare.ou.univariate(tdD$phy, tdD[['lnVs']], SE=0.02, pred=tdD['binpred'])
EVopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=EVtrees.bayou, chain.sigmoid = chainEV.sigmoid, chain.step=chainEV.step, chain.linear=chainEV.linear, cache = cacheEV)
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Dopt.allmodels <- lapply(1:100, optimizeModelsOverSimmap, strees.bayou=Dtrees.bayou, chain.sigmoid = chainD.sigmoid, chain.step=chainD.step, chain.linear=chainD.linear, cache = cacheD)
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
summarizeAllOpt <- function(opt.allmodels, cutoff=-Inf){
all.sigmoid <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid))
all.step <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step))
all.sigmoid0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.sigmoid0))
all.step0 <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.step0))
all.linear <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.linear))
all.tanhLin <- do.call(rbind, lapply(opt.allmodels, function(x) x$opt.tanhLin))
all.aics <- do.call(rbind, lapply(opt.allmodels, function(x) do.call(cbind, x$aics)))
all.aics[all.aics < cutoff] <- NA
boxplot(all.aics)
all.aics <- as.data.frame(all.aics)
t.test(all.aics$aic.sigmoid, all.aics$aic.linear, paired = TRUE)
hist(all.aics$aic.linear - all.aics$aic.sigmoid)
tmp <- reshape2::melt(all.aics)
aicws <- aicw(tmp$value)$w
aicws <- round(tapply(aicws, tmp$variable, sum),2)
return(list(sigmoid=all.sigmoid, step=all.step, sigmoid0=all.sigmoid0, step0 = all.step0, linear=all.linear, tanhLin=all.tanhLin, aics=all.aics, aicws=aicws))
}
sumEV <- summarizeAllOpt(EVopt.allmodels)
No id variables; using all as measure variables
sumD <- summarizeAllOpt(Dopt.allmodels)
No id variables; using all as measure variables
sumEV$aicws
aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.58 0.00 0.06 0.33 0.00 0.03
sumD$aicws
aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.27 0.01 0.38 0.26 0.02 0.07
dAICs <- apply(sumEV$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="EV", ylim=c(0,20))
cat("Mean EV dAIC values\n")
Mean EV dAIC values
apply(t(dAICs), 2, mean)
aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
0.6305072 43.1810208 3.6929463 1.1235774 41.1810208 4.6331318
dAICs <- apply(sumD$aics, 1, function(x) x-min(x, na.rm=TRUE))
par(mar=c(10, 4, 1,1))
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC", main="D")
boxplot(t(dAICs), las=2, xlab="Model", ylab="dAIC",main="D", ylim=c(0,8))
cat("Mean Deciduous dAIC values\n")
Mean Deciduous dAIC values
apply(t(dAICs), 2, mean)
aic.sigmoid aic.step aic.linear aic.sigmoid0 aic.step0 aic.tanhLin
3.8844676 9.3537055 0.5131179 1.7110603 7.3537055 3.3364835
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21)
x <- -600:200
abline(v=0)
dum <- lapply(1:nrow(sumEV$sigmoid), function(i) lines(x, sumEV$sigmoid[i, 3] + sumEV$sigmoid[i, 4]/(1 + exp(sumEV$sigmoid[i, 6]*(x-sumEV$sigmoid[i, 5]))), col="green", lty=1))
dum <- lapply(1:nrow(sumEV$sigmoid0), function(i) lines(x, sumEV$sigmoid0[i, 3] + sumEV$sigmoid0[i, 4]/(1 + exp(sumEV$sigmoid0[i, 5]*(x-0))), col="limegreen", lty=1))
#dum <- lapply(1:nrow(sumEV$tanhLin), function(i) lines(x, sumEV$tanhLin[i, 3] + sumEV$tanhLin[i,6]*x + sumEV$tanhLin[i,4]*tanh(x-sumEV$tanhLin[i,5]), col="green", lty=1))
dum <- lapply(1:nrow(sumD$linear), function(i) lines(x, sumD$linear[i,3] + sumD$linear[i,4]*x, col="orange2", lty=1))
dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i, 4]/(1 + exp(sumD$sigmoid0[i, 5]*(x-0))), col="orange", lty=1))
#dum <- lapply(1:nrow(sumD$sigmoid0), function(i) lines(x, sumD$sigmoid0[i, 3] + sumD$sigmoid0[i,4]/(1+exp(sumD$sigmoid0[i,5]*x)), col="tan", lty=1))
plot(td[['tmin.01']], td[['lnVs']], bg=c("tan","green")[as.numeric(td[['phenology']])], main="Temperature vs. vessel size", pch=21, xlab = "Temp (Cx10)", ylab="Log Vessel Diameter")
x <- -400:200
abline(v=0)
dum <- lapply(seq(1, length(chainEV.sigmoid$gen), 1), function(i) lines(x, chainEV.sigmoid$beta_left[i]+chainEV.sigmoid$beta_right[i]/(1 + exp(chainEV.sigmoid$beta_slope[i]*(x-chainEV.sigmoid$beta_center[i]))), col=makeTransparent(rgb(0, 1, 0), alpha=10)))
dum <- lapply(seq(1, length(chainD.linear$gen), 1), function(i) lines(x, chainD.linear$beta_intercept[i]+chainD.linear$beta_slope[i]*x, col=makeTransparent(rgb(0.9, 0.5, 0.2), alpha=10)))